Robotics
Too Much of a Good Thing: When sim2real Efforts Impede Policy Learning (And What to Do About It)
Kyle Morgenstein, Bharath Masetty, Stephen Welch et al. (+1 more)
While sim2real efforts are necessary for effective policy transfer to hardware, there is such a thing as too much of a good thing. We argue that sim2real efforts have led to misaligned incentives with policy learning, resulting in simulator lock in and poor policy exploration due to the unreasonable constraints imposed by the real world. We offer a diagnosis and explanation of the current status of the problem, and propose a potential solution via a sim2sim2real paradigm that leverages the
robot's kinematics as the sole design constraint.
Read the full abstract ▾
All authors Kyle Morgenstein, Bharath Masetty, Stephen Welch, Luis Sentis
Robotics
Generative Multi-Robot Motion Planning via Diffusion Modeling with Multi-Agent Reinforcement Learning Guidance
Suk Ki Lee, Venkata Sai Deepak Mutta and Hyunwoong Ko
Coordinating multiple
robots in shared environments requires generating feasible trajectories for each
agent while accounting for interactions among
agents. Centralized planning approaches become difficult to scale as the number of
robots increases, while decentralized approaches that allow each
agent to plan independently do not inherently account for inter-
agent interactions. This paper presents a framework for coordinated multi-
robot motion planning that combines decentralized generative trajectory planning with multi-
agent reinforcement learning (MARL)-based coordination. Each
robot independently generates candidate trajectories using a
diffusion model trained on single-
agent motion data, leveraging the generative model's ability to produce feasible and diverse trajectories. To reduce conflicts between
agents, a centralized value function trained via MARL guides the reverse
diffusion process through gradient-based steering, enabling interaction-aware trajectory generation without centralized joint planning or retraining of the generative model. This guidance follows an exponential tilting formulation, in which the value function biases the denoising distribution toward trajectories with higher expected multi-
agent return. The framework is evaluated in a simulated maze environment with four mobile
robots. Experimental results show that the proposed value-guided
diffusion planning reduces the inter-
agent interference rate from 55.4% to 41.8%, demonstrating that coordination can be effectively achieved while preserving the scalability of decentralized trajectory generation. These results suggest that MARL-based value guidance can effectively introduce coordination into decentralized generative planners without requiring a fully joint multi-
robot model.
Read the full abstract ▾
Robotics · Cross-listed from physics.med-ph
A Machine-to-Machine Knowledge-Guided LLM Agent for Generalizable Radiotherapy Treatment Planning
Md Mainul Abrar, Xun Jia and Yujie Chi
In this work, we propose a prototype machine-to-machine (M2M) knowledge-guided Large
Language Model (LLM) framework for automated radiotherapy treatment planning. In the proposed paradigm, Treatment Planning Parameter (TPP) distribution knowledge discovered by a Deep Reinforcement Learning (DRL)
agent is transferred to an LLM
agent through in-context learning, enabling autonomous iterative planning without human intervention. While standard LLM-based planning often lacks physical intuition and struggles with convergence, the integration of DRL-derived guidance constrains the
agent to a physically valid parameter space. Experimental evaluations are performed across three diverse planning scenarios: basic prostate cases, complex prostate configurations with increased organ-at-risk (OAR) constraints, and liver cases. The evaluation results demonstrate that the guided LLM
agent consistently achieves optimal planning scores while significantly reducing the number of iterations compared to unguided planning. Analysis of the final TPP configurations reveals that the
agent successfully learns a hierarchical priority of objectives, effectively restoring a logical "cause-and-effect" relationship between parameter tuning and dosimetric outcomes. Crucially, this prototype framework exhibits robust generalizability, maintaining high planning quality regardless of specific patient anatomy, treatment site, or initial plan quality. By bridging the specialized optimization of DRL with the adaptive
reasoning of LLMs, this M2M framework establishes a scalable foundation towards generalizable autonomous treatment planning, ultimately benefiting clinical practice in realistic environments.
Read the full abstract ▾
Robotics · Cross-listed from cs.CV
GABI: Geometry-Aware Boundary Integration for Spacecraft Segmentation
Iason Georgios Velentzas, Dhruv Ahuja and Panagiotis Tsiotras
Accurate
segmentation is crucial for autonomous spacecraft, as it directly affects downstream tasks related to 3D situational awareness. The harsh illumination conditions of space, however, produce images with high variability in appearance, hindering the generalization of
segmentation approaches across different spacecraft and environments. In this work, we propose GABI, a lightweight boundary-aware multi-task
segmentation architecture that augments a convolutional backbone with an auxiliary distance-field prediction head. The distance field provides dense
geometric supervision around object boundaries, encouraging the network to learn spatially consistent representations of spacecraft structures while maintaining low model complexity suitable for onboard perception systems. We evaluated GABI against both an established convolutional baseline and a heavier
transformer-based architecture. On the SPARK
benchmark, distance-field supervision improves the baseline by up to $5\%$ in Average Precision while achieving performance comparable to the
transformer models. In generalization experiments, GABI improves Average Precision by more than $50\%$ over the baseline. In cross-domain evaluation, the lightweight GABI variant performs within $5\%$ in IoU and F1-score of the heavier
transformer model while being approximately ten times smaller. At the same time, the heavier GABI variant surpasses the
transformer architectures while remaining nearly three times lighter.
Read the full abstract ▾
Robotics
From Cues to Horizons: Dynamic Risk Horizon Profiling for Trajectory Prediction
Xinyi Ning, Zilin Bian, Dachuan Zuo et al. (+2 more)
Accurate and reliable vehicle trajectory prediction is essential for safe autonomous driving. Recent studies have incorporated safety risk into trajectory prediction to quantify dangers posed by surrounding
agents. However, most risk-aware approaches use past risk information as a secondary signal to help guide decisions, overlooking its future
evolution and uncertainty. In this paper, we propose a risk horizon profiling (RHP) module that incorporates a continuous, learnable potential field model for risk-aware trajectory prediction. The RHP module calculates the spatial-temporal proximity of surrounding objects to profile risk distributions across future horizons, which supports better trajectory prediction by adaptively identifying what human drivers perceive as critical moments. We evaluate our method on two
datasets from different driving settings, highD for highway corridors and SHRP2 for urban streets, which cover diverse risk scenarios including safe, near-crash, and crash events. Compared to the baseline methods, our framework achieves a 25.0% reduction in 5s RMSE on the highD
dataset and a 29.1% reduction in 5s minFDE on SHRP2. These results indicate strong performance for both short and long horizon prediction and robust generalization across highway and urban scenarios. The proposed method enables more realistic AV path planning and strategic selection, thereby supporting safer autonomous driving and more advanced driver-assistance systems. The source code for this work is available at: https://github.com/bilab-nyu/RHP
Read the full abstract ▾
All authors Xinyi Ning, Zilin Bian, Dachuan Zuo, Semiha Ergan, Kaan Ozbay
Robotics
Coarse-to-Fine Compositional Diffusion for Long-Horizon Planning
Byoungwoo Park, Utkarsh A. Mishra, Jaemoo Choi et al. (+2 more)
Diffusion models provide strong priors for generating structured data, but many tasks require outputs beyond the scale on which these models are typically trained. Compositional generation addresses this by composing overlapping local plans from a pretrained short-horizon prior into a long-horizon output. However, standard composition primarily enforces agreement between neighboring local plans, yielding local consistency without directly specifying the global structure of the full composition. As a result, locally compatible plans may still form an implausible route, task sequence, or temporal
evolution. Existing methods improve global coherence by repeatedly propagating local consistency signals or by adding inference-time optimization, but these procedures become expensive as the number or dimensionality of local plans increases. We propose Coarse-to-Fine Compositional
Diffusion (CoFi), an inference-time sampler that separates global structure formation from local detail refinement. CoFi first aligns local denoised estimates around a shared coarse structure, producing a global scaffold that captures the long-range task-level arrangement. It then diffuses this scaffold to an intermediate noise level and denoises it with the same pretrained local prior, restoring local fine structure while preserving the scaffold-induced global coherence. Across long-horizon robotic planning, panoramic image generation, and long
video generation, CoFi not only improves both global coherence and local sample quality over prior compositional baselines, but also requires 2-8x fewer denoiser evaluations.
Read the full abstract ▾
All authors Byoungwoo Park, Utkarsh A. Mishra, Jaemoo Choi, Juho Lee, Yongxin Chen
Robotics
SafeVLA-Bench: A Benchmark for the Success-Safety Gap in Vision-Language-Action Models
Jialiang Fan, Weizhe Xu, Zijun Wang et al. (+3 more)
Vision-
language-action (VLA)
benchmarks measure whether a policy completes a requested manipulation task, but binary success can hide safety violations along the trajectory: a policy may reach the goal while applying excessive contact, disturbing bystander objects, destabilizing a held object, or entering
robot self-contact. We present SafeVLA-Bench, a post-hoc safety-evaluation framework for existing simulator-based VLA
benchmarks that reveals violations missed by success-only evaluation. It encodes task-aware safety requirements as Signal Temporal Logic (STL) invariants with quantitative robustness semantics. Alongside native success, it reports the safety rate and the success-but-unsafe rate (SBU) used in prior safety evaluations, and introduces the Violation Severity Index (VSI), a bounded worst-violation depth score. We instantiate SafeVLA-Bench on LIBERO and RoboCasa-365, evaluating twenty-seven policy-
benchmark entries across tabletop and kitchen manipulation tasks. High task success does not imply safe execution: the fifteen tabletop policies above 90% mean success still have 18-28% unsafe-episode rates, and 38-56% of successful RoboCasa-365 rollouts violate at least one active safety clause. A post-training case study further shows that SafeVLA-Bench can be used to improve policy safety. Project page: https://safevla.org
Read the full abstract ▾
All authors Jialiang Fan, Weizhe Xu, Zijun Wang, Oleg Sokolsky, Insup Lee, Fanxin Kong
Robotics
STEM: Semantic Target Search and Exploration using MAVs in Cluttered Environments
Nikhil Sethi, Max Lodel, Laura Ferranti et al. (+2 more)
Autonomous target search is crucial for deploying Micro Aerial Vehicles (MAVs) in emergency response and rescue missions. Existing approaches either focus on 2D semantic
navigation in structured environments – which is less effective in complex 3D settings, or on robotic exploration in cluttered spaces – which often lacks the semantic
reasoning needed for efficient target search. This paper overcomes these limitations by proposing a novel framework that utilizes a semantically-guided viewpoint planner to minimize target search and exploration time in unstructured 3D environments using an MAV. Specifically, we develop a combinatorial planner that generates efficient semantic exploration plans by prioritizing viewpoints that likely lead to the target. To guide the planner towards the target, an active perception pipeline is developed that propagates semantic priorities of observed objects into neighboring frontier voxels for computing semantic information gains of frontier viewpoints. In addition, we demonstrate how LLM-based similarity scores can be leveraged as semantic priority input to our pipeline. Evaluations in two distinct simulation environments show that the proposed method consistently outperforms baselines by quickly finding the target while maintaining reasonable exploration times. Real-world experiments with an MAV further demonstrate the method's ability to handle practical constraints like limited battery life, small sensor range, and semantic uncertainty.
Read the full abstract ▾
All authors Nikhil Sethi, Max Lodel, Laura Ferranti, Robert Babuška, Javier Alonso-Mora
Robotics
Beyond Pure Sampling: Hybrid Optimization Mechanisms for Non-Convex Model Predictive Control
Yuichiro Aoyama, Minchan Jung, Akash Ratheesh et al. (+1 more)
This paper investigates the optimization mechanisms of non-convex Model Predictive Control (MPC) using the Maximum
Entropy Differential Dynamic Programming (ME-DDP) framework. Navigating non-convex cost landscapes induced by nonlinear dynamics, multiple obstacles, etc. remains a fundamental challenge in robotics, where gradient-based methods frequently converge to suboptimal local minima. We demonstrate a dual-step optimization mechanism designed to overcome these traps. (1) an initial phase of using DDP to exploit the gradient of the cost landscape, followed by (2) disruption of the optimization via sampling from policies characterized by the inverse Hessian of the action-value function. We provide a rigorous analysis of this sampling mechanism of three ME-DDP variants: Unimodal Gaussian ME-DDP,
Multimodal Gaussian ME-DDP, and Stein Variational DDP. Furthermore, with
navigation tasks of four robotic systems under cluttered environments, we conduct extensive benchmarking of three variants of the ME-DDP, against deterministic DDP, and one of the most successful sampling-based schemes, Model Predictive Path Integral (MPPI) control with three policy parameterizations and update laws that correspond to those of ME-DDPs. The results show that in low-dimensional systems where the cost landscapes are relatively simple and local information is sufficiently representative, our framework consistently outperforms MPPIs. In high-dimensional systems, MPPI can occasionally discover aggressive maneuvers that enable it to steer the systems faster than DDP-based methods, whereas our method maintains a higher, more stable success rate. Finally, we validate the practical efficacy of the framework through hardware experiments with a quadrotor navigating a dense, non-convex obstacle field, confirming the robustness of the proposed framework for real-world deployment.
Read the full abstract ▾
All authors Yuichiro Aoyama, Minchan Jung, Akash Ratheesh, Evangelos A. Theodorou
Robotics
Infeasible optimization problems and the hierarchical augmented Lagrangian method in imitation learning
Roland Andrews, Justin Carpentier and Ajay Sathya
Imitation learning (IL) is an effective approach to train complex robotics policies. Recent works have introduced hard constraints into imitation-learning optimization problems to ensure safety, stability, and robustness of the learned policy. However, we argue that these constraints are sometimes infeasible, which can lead to unstable or difficult training dynamics. We study a simple remedy for such situations based on recent theoretical results on the augmented Lagrangian method in infeasible settings. We show that our approach drives the learned policy toward the solution of a closest-feasible constrained IL problem with desirable properties. The method is illustrated on a toy driving example with a total-acceleration constraint and pedestrian-safety constraints, a setting in which infeasibility can naturally arise while still allowing a safe learned policy.
Read the full abstract ▾
Robotics
BEVIO: Efficient Bird's-Eye-View based Sparse-Update Visual-Inertial Odometry for Lunar Day-Night Navigation
Mohit Singh, Shehryar Khattak, Ashish Goel et al. (+3 more)
Visual-Inertial Odometry (VIO) provides smooth, high-rate state estimates and has been widely used for robotic
navigation in both terrestrial and planetary applications. However, its performance is typically dependent on the frequency of visual updates, which is a challenge for planetary rovers operating under extreme resource constraints and low frame rates. This work investigates enabling reliable VIO with very sparse visual updates for lunar rover applications, addressing both day and night-time operations where feature associations become especially difficult under self-illumination conditions. We propose a Bird's Eye View (BEV)-based image matching scheme that remains robust to larger inter-frame motions and more reliable feature matching despite significant visual appearance changes. We extensively evaluate our proposed approach, BEVIO, through high-fidelity photorealistic lunar and real-time robotic experiments conducted using a half-scale lunar rover, in a long-term day-night deployment at Plaster City, CA, USA. The results demonstrate that our method enables reliable day and nighttime self-illuminated traverses at visual update rates as low as 0.25 Hz, underscoring its suitability for
navigation on power- and compute-limited lunar rovers.
Read the full abstract ▾
All authors Mohit Singh, Shehryar Khattak, Ashish Goel, Michael Paton, Kostas Alexis, Issa A. Nesnas
Robotics
Shape Your Body: Value Gradients for Multi-Embodiment Robot Design
Nico Bohlinger and Jan Peters
We propose to turn generalist multi-embodiment value functions into reusable models for
robot design. Instead of running a new reinforcement learning co-design loop for each
robot, we first train an embodiment-aware policy and value function across many
robot designs. After training, the frozen value function is used as a differentiable surrogate to optimize candidate embodiments through value gradients. We evaluate our approach across different
robot design settings, from perturbed single
robots to held-out
robots across morphology classes, with single models trained on up to 50
robots and design spaces of over 1100 continuous embodiment parameters. Beyond optimizing complete embodiments, we show that value gradients can identify performance-limiting design and control parameters, enabling both the optimization and the analysis of new
robot designs.
Read the full abstract ▾
Robotics
SKIP: Sparse Keyframe Interpolation Paradigm for Efficient Embodied World Models
Ziheng He, Yixiang Chen, Ning Yang et al. (+11 more)
Embodied world models have emerged as a promising paradigm in robotics by predicting how
robot actions affect the surrounding scene. However, the rollout inference remains computationally expensive in pixel space, as long-horizon manipulation
videos typically have to be generated frame by frame. This cost cannot be easily reduced by indiscriminately dropping frames, since downstream policies rely on complete preservation of sparse task-relevant events such as approach, contact, grasp, and release. To address this challenge, we propose Sparse Keyframe Interpolation Paradigm (SKIP), an event-preserving sparse-to-dense framework that avoids dense frame-by-frame generation. SKIP first identifies task-relevant keyframes by leveraging
robot-aware
multimodal features. It then synthesizes only these keyframes with a sparse
video diffusion model. A learned gap predictor and an action-conditioned interpolator subsequently reconstruct the missing intervals according to the
robot actions. On LIBERO, SKIP generates dense rollouts $4.16\times$ faster than a dense baseline while improving visual fidelity and reducing aggregate FVD by $89.0\%$. Importantly, SKIP-generated
videos are effective policy-training data. Even when they fully replace real demonstrations, $\pi_{0.5}$ success drops only $1.3$ pp in LIBERO simulation and $6.7$ pp on the real
robot, whereas fully dense frame-by-frame generation collapses by $48$ to $58$ pp.
Read the full abstract ▾
All authors Ziheng He, Yixiang Chen, Ning Yang, Zhanqian Wu, Qisen Ma, Yuan Xu, Jiabing Yang, Peiyan Li, Xiangnan Wu, Xiaofeng Wang, Zheng Zhu, Jing Liu, Nianfeng Liu, Yan Huang
Robotics
Global-Local Attention Decomposition for Terrain Encoding in Humanoid Perceptive Locomotion
Shengcheng Fu, Yang Zhang, Zhanxiang Cao et al. (+4 more)
Although reinforcement learning has significantly advanced
humanoid locomotion, perceptive policies still struggle on sparse-foothold terrain and constrained environments. Success in these scenarios requires both broad terrain awareness and precise foothold selection, two perceptual roles that conventional encoders often entangle. To address this challenge, we propose Global-Local
Attention Decomposition (GLAD) for terrain encoding in
humanoid locomotion. Realized by a coarse-to-fine encoder over a
robot-centric elevation map, GLAD explicitly separates these objectives: a global
attention branch uses
attention pooling to summarize the surrounding terrain context, while a local
attention branch sparsifies the local features by terrain saliency and applies state-conditioned
attention to encode precise foothold-relevant geometry. This explicit
attention decomposition prevents the dilution of fine-grained spatial cues while reducing training overhead. Experiments demonstrate that GLAD enables reliable locomotion over challenging gaps, stepping stones, and stairs. Furthermore, the learned policy exhibits emergent terrain-responsive behaviors, autonomously following narrow paths and avoiding obstacles under forward-velocity commands alone, without explicit
navigation planners. In real-world deployment on a Unitree G1
humanoid robot using onboard
LiDAR, the proposed method achieves robust zero-shot sim-to-real transfer across diverse sparse-foothold and obstacle-rich domains.
Read the full abstract ▾
All authors Shengcheng Fu, Yang Zhang, Zhanxiang Cao, Liyun Yan, Yizhi Chen, Yunpeng Yin, Yue Gao
Robotics
Dynamic Resilient Spatio-Semantic Memory with Hybrid Localization for Mobile Manipulation
Zhijie Yan, Shufei Li, Ze Zhang et al. (+3 more)
Reliable mobile manipulation in dynamic indoor environments requires a scene representation that remains geometrically consistent, semantically queryable, and computationally bounded as the environment changes. Existing systems often rely on pre-built maps, static-scene assumptions, or highly accurate camera poses, which can lead to stale or misaligned scene information when target objects are relocated or pose estimates are corrected. This paper presents DREAM, a real-
robot mobile manipulation framework that integrates perception,
memory, localization,
navigation, and manipulation in previously unseen indoor environments without a pre-built map. DREAM constructs an online spatio-semantic voxel
memory from RGB-D observations registered by a
LiDAR-inertial-visual SLAM backend. It further introduces pose-graph-aware Redundancy-Aware
Memory Pruning (RMP) to update historical observations after pose corrections while keeping long-horizon observation history bounded. For target localization and reacquisition, DREAM combines
language-conditioned 3D
retrieval, open-vocabulary image detection, and
multimodal large
language model based semantic verification. Real-
robot experiments in four dynamic indoor laboratory scenes show that DREAM improves long-horizon task success rates from 40%-60% with DynaMem to 55%-70%, while maintaining a
memory footprint of 0.37-0.63 GB and an online
memory-update time of 0.43-0.53 s across scenes.
Read the full abstract ▾
All authors Zhijie Yan, Shufei Li, Ze Zhang, Xin Liu, Yuhang Zheng, Zuoxu Wang
Robotics · Cross-listed from cs.OS
Edge-Based QoS-Aware Adaptive Task Placement: A Closed-Loop Control in Multi-Robot Systems
Thien Tran, Jonathan Kua, Thuong Hoang et al. (+3 more)
Multi-
robot systems (MRS) increasingly offload compute-intensive perception tasks to edge nodes to meet strict time-sensitive Quality-of-Service (QoS) constraints. However, static task orchestration on a shared edge node can severely degrade QoS due to network latency, jitter, and edge-resource contention. We present a pilot edge-centric MRS testbed using Raspberry Pi nodes to evaluate a camera-to-manipulator pipeline under three modes: local execution, static offloading, and a QoS-aware Adaptive Task Placement (ATP) controller. ATP scores candidate placements using a multi-metric cost (normalized latency, CPU utilization, and switching overhead) over two-second control windows. The closed-loop visual servoing testbed is instrumented with sub-millisecond clock synchronization, network emulation, and detailed monitoring of multiple metrics across nodes to capture realistic jitter. Experimental results under compute-stress and network-fault scenarios show that static edge offloading reduces on-board CPU load but amplifies tail latency and deadline misses. In contrast, the QoS-aware ATP controller, by switching task placement based on measured latency and utilization thresholds, consistently lowers deadline violations and tail latency. Overall, the results position ATP as a practical edge-side control primitive for MRS and concrete design guidelines for Cloud-Edge Robotics deployments within the broader cloud-fog automation, while motivating QoS-aware multi-objective workload orchestration for industrial cyber-physical systems.
Read the full abstract ▾
All authors Thien Tran, Jonathan Kua, Thuong Hoang, Minh Tran, Honghao Lyu, Jiong Jin
Robotics · Cross-listed from cs.HC
A Four-Tier Communication Architecture and Sim-to-Real Validation of a Graphical Open-Source Platform for Robotic Engineering Education
Thien Tran, Khang Duong, Minh Tran et al. (+3 more)
The persistent challenge in scaling authentic manipulator education within university laboratories is a structural dichotomy: commercial digital twins are often cost-prohibitive and rigidly scripted, whereas open-source robotics middleware (ROS) imposes steep technical and syntax barriers for novices. To resolve this logistical and educational friction, this paper proposes a scalable four-tier communication architecture tailored for sustainable robotic curricula. Rather than focusing on software application design, our study examines the underlying data exchange mechanisms required to bridge visual conceptual environments with physical robotic endpoints, utilizing the Graphical Open-Source Platform (GOSP) as a reference implementation. Our work details the framework's technical integration of 3D visual armature modeling with a robust ROS middleware backend, emphasizing the serialization, routing, and encapsulation of intricate communication routines. Preliminary sim-to-real validation using multi-axis spatial trajectories confirms that encapsulating these communication pipelines provides sufficient fidelity and a hardware-agnostic pathway. By bridging virtual design and physical execution, our architectural blueprint offers a viable infrastructure for engineering education.
Read the full abstract ▾
All authors Thien Tran, Khang Duong, Minh Tran, Jonathan Kua, Thuong Hoang, Jiong Jin
Robotics
PACE: Phase-Aware Chunk Execution for Robot Policies with Action Chunking
Junnan Nie, Jiayi Li, Chenghao Liu et al. (+5 more)
Recent
vision-
language-action and
diffusion-based
robot policies often use action chunking, where each policy query predicts a sequence of future actions and the
robot executes an open-loop prefix before re-querying. While this interface improves local motion continuity, deployment still requires choosing the execution horizon: how much of each predicted chunk should be executed before acquiring a new observation. However, our experiments show that success is strongly task-dependent and non-monotonic with respect to the execution horizon, making a single constant horizon an unreliable deployment rule. We propose PACE (Phase-Aware Chunk Execution), a training-free test-time execution method that selects the execution horizon online from the predicted chunk itself. PACE exploits the phase-dependent kinematic structure of manipulation trajectories by identifying low-speed transition points in the predicted speed profile and using them as candidate replanning boundaries. Because PACE uses only the predicted action chunk, it is plug-and-play and requires no retraining or access to policy internals. We validate PACE through large-scale evaluations in both simulation and real-
robot settings. On 50 RoboTwin2.0 tasks, PACE raises the average success rate from 57.8% to 64.2%. In real-
robot experiments on bimanual ALOHA and single-arm Franka platforms, PACE improves the average task score from 60.7 to 77.7 and the average success rate from 50.7% to 70.4%. Ablations and rollout-level analyses show that PACE adapts execution horizons across manipulation phases, shortening near transitions while preserving longer execution during coherent motion.
Read the full abstract ▾
All authors Junnan Nie, Jiayi Li, Chenghao Liu, Junyi Lao, Jiachen Zhang, Tianle Zhang, Liang Lin, Songfang Huang
Robotics
DriveAnchor: Progressive Anchor-based Flow Learning for Autonomous Driving Planning
Limin Yan, Haoyun Tang, Yutao Qiu et al. (+3 more)
Autonomous-driving planners must generate route- and maneuver-compliant trajectories under a limited decoder-call budget while adapting offline to black-box static-collision feedback. We propose DriveAnchor, a three-stage framework built around a shared vocabulary of recorded trajectories and a continuous trajectory interface. The vocabulary supports flow-matching (FM) residual learning, Energy Field (EF) corridor-conditioned initialization, and anchor-guided zeroth-order reward fine-tuning. The reward update operates in trajectory space without policy likelihoods or reward gradients, enabling offline adaptation of a deterministic decoder. On sampled subsets of nuPlan Val14 and Test14-hard per simulation mode, DriveAnchor achieves the highest reactive scores among the compared methods: 81.50 and 65.74, respectively. We further integrate the trajectory generator into a real vehicle's planning pipeline and demonstrate its operation on urban roads. On 10,000 held-out internal scenarios, under the original non-EF prior and a fixed two-call FM budget, reward fine-tuning reduces short- and long-horizon static candidate collision rates from 27.22% and 56.54% to 2.94% and 7.15%, respectively, and improves 8-s best-of-set minADE from 2.27 to 1.34 m. In a separate junction diagnostic on checkpoints before Stage 3, one and two EF calls raise corridor-guidance satisfaction from 22.10% to 74.13% and 87.92%, respectively. Additional ablations support the benefits of real-trajectory priors and anchor-guided exploration. In real-vehicle tests, trajectory generation takes 2.06 ms on average. Real-vehicle deployment
videos are available on the project page: https://driveanchor-meituan.github.io/
Read the full abstract ▾
All authors Limin Yan, Haoyun Tang, Yutao Qiu, Hongqing Liu, Zhandong Mei, Haoyu Xu
Robotics
PaCo-VLA: Passivity-Shielded Compliance Prior for Contact-Rich Vision-Language-Action Manipulation
Haofan Cao, Zhaoyang Li and Zhichao You
Contact-rich manipulation demands both high-level semantic
reasoning and the safe regulation of high-frequency contact dynamics. While
Vision-
Language-Action (VLA) models provide unprecedented semantic generalization, their low-rate outputs lack the reliability required for direct plant authority in force-sensitive tasks. To bridge this semantic-to-control gap, we introduce PaCo-VLA, a passivity-shielded compliance prior that recasts the VLA interface. Rather than trusting VLAs with direct motor commands, PaCo-VLA treats network outputs as task-level compliance proposals: semantic bindings, task stages, and admittance schedules. A high-frequency, proposal-independent passivity shield governs these proposals through energy-tank accounting and boundary checks, preventing invalid, stale, or unverified model predictions from bypassing low-level contact physics. This decoupled architecture also enables causal evaluation, isolating semantic contributions from
geometric shortcuts. Extensive simulated and real-world connector-insertion experiments demonstrate that PaCo-VLA achieves superior precision over unshielded VLA baselines, sustaining zero passivity violations even under
adversarial compliance shifts. This framework establishes a provably sampled-passive runtime contract at the admittance port and provides a runtime interface for deploying foundation models in contact-rich domains.
Read the full abstract ▾
Robotics
A passive universal grasping mechanism based on an everting shell
Mythra V. S. Balakuntala, Safvan Palathingal and G. K. Ananthasuresh
A passive monolithic compliant grasping mechanism that works based on the eversion of an elastically deformable bistable shell is conceptualized. It comprises grasping arms made of beam segments that work in conjunction with the everting shell. The grasper is capable of picking up a stiff object of any shape up to a maximum size and weight. The bistable shell everts upon contact with the object to enable the grasping arms envelop the object forming an enclosure. The mechanism then stays in that configuration until it is actuated again to turn the shell back to its original configuration and thereby opening the enclosure to release the object. The stiffness of the arms decides the payload of the mechanism. The size of the arms decides the largest object that can be grasped and held. The arms have distributed compliance so that they can conform to the shape of the object without applying undue force on it.
Read the full abstract ▾
Robotics
Adaptive PD Gains for Energy-Conscious Control in Physical Human-Robot Interaction
Danyal Saqib, Francisco Andrade Chavez and Marie Charbonneau
Compliant force or torque control are approaches often investigated to achieve safe physical human-
robot interaction (pHRI). However, these approaches have limitations. Force control requires a
robot to be equipped with external force sensors to track the amplitude and direction of applied forces. Torque control requires torque sensing or estimation in each joint. As this is not available on every
robot, energy-based approaches offer a promising alternative. Such approaches aim to achieve safe pHRI by limiting the mechanical energy of the
robot. Current schemes leveraging an energy-based approach tend to have a complex implementation, and some may require further stability verification. We hence propose an adaptive proportional-derivative (PD) controller that can limit a
robot's energy under any given limit to achieve safe pHRI. The proposed controller can limit both the kinetic and potential energy of a
robot, and the behaviour of the controller gains can be shaped using various parameters, defining precisely the cutoff limit and sharpness. We construct a stability proof for the controller and define a condition to ensure the controller's stability. The proposed controller's behaviour and compliance are tested on the TALOS
robot from PAL Robotics both in simulation and on hardware, verifying the expected compliant and energy-limiting behaviour of the controller.
Read the full abstract ▾
Robotics
ROG-Grasp: Root-Oriented Geometry for Robotic Grasping and Placement
Zijian An, Augustus Sroka, Ran Yang et al. (+8 more)
Orientation-aware manipulation is essential in post-harvest agricultural processing, where produce must be grasped and placed in consistent configurations. This paper presents ROG-Grasp, a geometry-based robotic grasping and placement framework that estimates the produce orientation from root surface geometry using RGB-D perception. A YOLO-based root detector and point cloud plane fitting are used to infer the root normal, enabling stable grasp pose generation and orientation-constrained Cartesian motion planning. Experiments on tomatoes and onions demonstrate high success rates and stable execution time in both isolated and cluttered scenarios. Compared with
vision-
language-action (VLA) policies, the proposed method achieves more reliable and accurate grasp completion with faster execution. These results highlight the effectiveness of geometry-driven perception for practical orientation-controlled manipulation tasks. A
video of our paper is available online https://youtu.be/Ir2UtGODdMo.
Read the full abstract ▾
All authors Zijian An, Augustus Sroka, Ran Yang, Bill Cai, Satoru Eto, Brian Poon, Kelvin Cai, Shijie Geng, Feng Liu, Yiming Feng, Lifeng Zhou