Robotics
Reinforcement Learning for Robotic Safe Control with Force Sensing
Nan Lin, Linrui Zhang, Yuxuan Chen et al. (+5 more)
For the task with complicated manipulation in unstructured environments, traditional hand-coded methods are ineffective, while reinforcement learning can provide more general and useful policy. Although the reinforcement learning is able to obtain impressive results, its stability and reliability is hard to guarantee, which would cause the potential safety threats. Besides, the transfer from simulation to real world also will lead in unpredictable situations. To enhance the safety and reliability of
robots, we introduce the force and haptic perception into reinforcement learning. Force and tactual sensation play key roles in robotic dynamic control and human-
robot interaction. We demonstrate that the force-based reinforcement learning method can be more adaptive to environment, especially in sim-to-real transfer. Experimental results show in object pushing task, our strategy is safer and more efficient in both simulation and real world, thus it holds prospects for a wide variety of robotic applications.
Read the full abstract ▾
All authors Nan Lin, Linrui Zhang, Yuxuan Chen, Zhenrui Chen, Yujun Zhu, Ruoxi Chen, Peichen Wu, Xiaoping Chen
Robotics
Constructing the Umwelt: Cognitive Planning through Belief-Intent Co-Evolution
Shiyao Sang
This paper challenges a prevailing epistemological assumption in End-to-End Autonomous Driving: that high-performance planning necessitates high-fidelity world reconstruction. Inspired by cognitive science, we propose the Mental
Bayesian Causal World Model (MBCWM) and instantiate it as the Tokenized Intent World Model (TIWM), a novel cognitive computing architecture. Its core philosophy posits that intelligence emerges not from pixel-level objective fidelity, but from the Cognitive Consistency between the
agent's internal intentional world and physical reality. By synthesizing von Uexküll's $\textit{Umwelt}$ theory, the neural assembly hypothesis, and the triple causal model (integrating symbolic deduction, probabilistic induction, and force dynamics) into an end-to-end
embodied planning system, we demonstrate the feasibility of this paradigm on the nuPlan
benchmark. Experimental results in open-loop validation confirm that our Belief-Intent Co-
Evolution mechanism effectively enhances planning performance. Crucially, in closed-loop simulations, the system exhibits emergent human-like cognitive behaviors, including map affordance understanding, free exploration, and self-recovery strategies. We identify Cognitive Consistency as the core learning mechanism: during long-term training, belief (state understanding) and intent (future prediction) spontaneously form a self-organizing
equilibrium through implicit computational replay, achieving semantic
alignment between internal representations and physical world affordances. TIWM offers a neuro-symbolic, cognition-first alternative to reconstruction-based planners, establishing a new direction: planning as active understanding, not passive reaction.
Read the full abstract ▾
Robotics
Real-DRL: Teach and Learn in Reality
Yanbing Mao, Yihao Cai and Lui Sha
This paper introduces the Real-DRL framework for safety-critical autonomous systems, enabling runtime learning of a deep reinforcement learning (DRL)
agent to develop safe and high-performance action policies in real plants (i.e., real physical systems to be controlled), while prioritizing safety! The Real-DRL consists of three interactive components: a DRL-Student, a PHY-Teacher, and a Trigger. The DRL-Student is a DRL
agent that innovates in the dual self-learning and teaching-to-learn paradigm and the real-time safety-informed batch sampling. On the other hand, PHY-Teacher is a physics-model-based design of action policies that focuses solely on safety-critical functions. PHY-Teacher is novel in its real-time patch for two key missions: i) fostering the teaching-to-learn paradigm for DRL-Student and ii) backing up the safety of real plants. The Trigger manages the interaction between the DRL-Student and the PHY-Teacher. Powered by the three interactive components, the Real-DRL can effectively address safety challenges that arise from the unknown unknowns and the Sim2Real gap. Additionally, Real-DRL notably features i) assured safety, ii) automatic hierarchy learning (i.e., safety-first learning and then high-performance learning), and iii) safety-informed batch sampling to address the learning experience imbalance caused by corner cases. Experiments with a real quadruped
robot, a quadruped
robot in NVIDIA Isaac Gym, and a cart-pole system, along with comparisons and ablation studies, demonstrate the Real-DRL's effectiveness and unique features.
Read the full abstract ▾
Robotics · Cross-listed from cs.LG
Pelican-VL 1.0: A Foundation Brain Model for Embodied Intelligence
Yi Zhang, Che Liu, Xiancong Ren et al. (+20 more)
This report presents Pelican-VL 1.0, a new family of open-source
embodied brain models with parameter scales ranging from 7 billion to 72 billion. Our explicit mission is clearly stated as: To embed powerful intelligence into various embodiments. Pelican-VL 1.0 is currently the largest-scale open-source
embodied multimodal brain model. Its core advantage lies in the in-depth integration of data power and intelligent adaptive learning mechanisms. Specifically, metaloop distilled a high-quality
dataset from a raw
dataset containing 4+ billion
tokens. Pelican-VL 1.0 is trained on a large-scale cluster of 1000+ A800 GPUs, consuming over 50k+ A800 GPU-hours per checkpoint. This translates to a 20.3% performance uplift from its base model and outperforms 100B-level open-source counterparts by 10.6%, placing it on par with leading proprietary systems on well-known
embodied benchmarks. We establish a novel framework, DPPO (Deliberate Practice Policy Optimization), inspired by human metacognition to train Pelican-VL 1.0. We operationalize this as a metaloop that teaches the AI to practice deliberately, which is a RL-Refine-Diagnose-SFT loop.
Read the full abstract ▾
All authors Yi Zhang, Che Liu, Xiancong Ren, Hanchu Ni, Shuai Zhang, Zeyuan Ding, Jiayu Hu, Hanzhe Shan, Zhenwei Niu, Zhaoyang Liu, Shuang Liu, Yue Zhao, Junbo Qi, Qinfan Zhang, Dengjie Li, Yidong Wang, Jiachen Luo, Yong Dai, Zenglin Xu, Bin Shen, Qifan Wang, Jian Tang, Xiaozhu Ju
Robotics
Digital Twin based Automatic Reconfiguration of Robotic Systems in Smart Environments
Angelos Alexopoulos, Agorakis Bompotas, Nikitas Rigas Kalogeropoulos et al. (+3 more)
Robotic systems have become integral to smart environments, enabling applications ranging from urban surveillance and automated agriculture to industrial automation. However, their effective operation in dynamic settings - such as smart cities and precision farming - is challenged by continuously evolving topographies and environmental conditions. Traditional control systems often struggle to adapt quickly, leading to inefficiencies or operational failures. To address this limitation, we propose a novel framework for autonomous and dynamic reconfiguration of robotic controllers using Digital Twin technology. Our approach leverages a virtual replica of the
robot's operational environment to simulate and optimize movement trajectories in response to real-world changes. By recalculating paths and control parameters in the Digital Twin and deploying the updated code to the physical
robot, our method ensures rapid and reliable adaptation without manual intervention. This work advances the integration of Digital Twins in robotics, offering a scalable solution for enhancing autonomy in smart, dynamic environments.
Read the full abstract ▾
All authors Angelos Alexopoulos, Agorakis Bompotas, Nikitas Rigas Kalogeropoulos, Panagiotis Kechagias, Athanasios P. Kalogeras, Christos Alexakos
Robotics · Cross-listed from cs.CV
Self-Improving Vision-Language-Action Models with Data Generation via Residual RL
Wenli Xiao, Haotian Lin, Andy Peng et al. (+9 more)
Supervised fine-tuning (SFT) has become the de facto post-training strategy for large
vision-
language-action (VLA) models, but its reliance on costly human demonstrations limits scalability and generalization. We propose Probe, Learn, Distill (PLD), a three-stage plug-and-play framework that improves VLAs through residual reinforcement learning (RL) and distribution-aware data collection. In Stage 1, we train lightweight residual actors to probe failure regions of the VLA generalist. In Stage 2, we use a hybrid rollout scheme that aligns collected trajectories with the generalist's deployment distribution while capturing recovery behaviors. In Stage 3, we distill the curated trajectories back into the generalist with standard SFT. PLD achieves near-saturated 99% task success on LIBERO, over 50% gains in SimplerEnv, and 100% success on real-world Franka and YAM arm manipulation tasks. Ablations show that residual probing and distribution-aware replay are key to collecting deployment-aligned data that improves both seen and unseen tasks, offering a scalable path toward self-improving VLA models.
Read the full abstract ▾
All authors Wenli Xiao, Haotian Lin, Andy Peng, Haoru Xue, Tairan He, Yuqi Xie, Fengyuan Hu, Jimmy Wu, Zhengyi Luo, Linxi "Jim" Fan, Guanya Shi, Yuke Zhu
Robotics
Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail
NVIDIA: Yan Wang, Wenjie Luo, Junjie Bai et al. (+39 more)
End-to-end architectures trained via imitation learning have advanced autonomous driving by scaling model size and data, yet performance remains brittle in safety-critical long-tail scenarios where supervision is sparse and causal understanding is limited. We introduce Alpamayo-R1 (AR1), a
vision-
language-action model (VLA) that integrates Chain of Causation
reasoning with trajectory planning for complex driving scenarios. Our approach features three key innovations: (1) the Chain of Causation (CoC)
dataset, built through a hybrid auto-labeling and human-in-the-loop pipeline producing decision-grounded, causally linked
reasoning traces aligned with driving behaviors; (2) a modular VLA architecture combining Cosmos-Reason, a
vision-
language model pre-trained for Physical AI, with a
diffusion-based trajectory decoder that generates dynamically feasible trajectories in real time; (3) a multi-stage training strategy using supervised fine-tuning to elicit
reasoning and reinforcement learning (RL) to enforce
reasoning-action consistency and optimize
reasoning quality. AR1 achieves up to a 12% improvement in planning accuracy on challenging cases compared to a trajectory-only baseline, with a 35% reduction in close encounter rate in closed-loop simulation. RL post-training improves
reasoning quality by 45% and
reasoning-action consistency by 37%. Model scaling from 0.5B to 7B parameters shows consistent improvements. On-vehicle road tests confirm real-time performance (99 ms latency) and successful urban deployment. By bridging interpretable
reasoning with precise control, AR1 demonstrates a practical path towards Level 4 autonomous driving. Model weights are available at https://huggingface.co/nvidia/Alpamayo-R1-10B with inference code at https://github.com/NVlabs/alpamayo.
Read the full abstract ▾
All authors NVIDIA: Yan Wang, Wenjie Luo, Junjie Bai, Yulong Cao, Tong Che, Ke Chen, Yuxiao Chen, Jenna Diamond, Yifan Ding, Wenhao Ding, Liang Feng, Greg Heinrich, Jack Huang, Peter Karkus, Boyi Li, Pinyi Li, Tsung-Yi Lin, Dongran Liu, Ming-Yu Liu, Langechuan Liu, Zhijian Liu, Jason Lu, Yunxiang Mao, Pavlo Molchanov, Lindsey Pavao, Zhenghao Peng, Mike Ranzinger, Ed Schmerling, Shida Shen, Yunfei Shi, Sarah Tariq, Ran Tian, Tilman Wekel, Xinshuo Weng, Tianjun Xiao, Eric Yang, Xiaodong Yang, Yurong You, Xiaohui Zeng, Wenyuan Zhang, Boris Ivanovic, Marco Pavone
Robotics
SpikeATac: A Multimodal Tactile Finger with Taxelized Dynamic Sensing for Dexterous Manipulation
Eric T. Chang, Peter Ballentine, Zhanpeng He et al. (+8 more)
In this work, we introduce SpikeATac, a
multimodal tactile finger combining a taxelized and highly sensitive dynamic response (PVDF) with a static transduction method (capacitive) for
multimodal touch sensing. Named for its `spiky' response, SpikeATac's 16-taxel PVDF film sampled at 4 kHz provides fast, sensitive dynamic signals to the very onset and breaking of contact. We characterize the sensitivity of the different modalities, and show that SpikeATac provides the ability to stop quickly and delicately when grasping fragile, deformable objects. Beyond parallel grasping, we show that SpikeATac can be used in a learning-based framework to achieve new capabilities on a dexterous multifingered
robot hand. We use reinforcement learning from human feedback to fine-tune the behavior of a policy to modulate force. Our hardware platform and learning pipeline together enable a difficult dexterous and contact-rich task that has not previously been achieved: in-hand manipulation of fragile objects.
Videos are available at https://roamlab.github.io/spikeatac/ .
Read the full abstract ▾
All authors Eric T. Chang, Peter Ballentine, Zhanpeng He, Do-Gon Kim, Kai Jiang, Hua-Hsuan Liang, Joaquin Palacios, William Wang, Pedro Piacenza, Ioannis Kymissis, Matei Ciocarlie
Robotics
A Multi-Modal Neuro-Symbolic Approach for Spatial Reasoning-Based Visual Grounding in Robotics
Simindokht Jahangard, Mehrzad Mohammadi, Abhinav Dhall et al. (+1 more)
Visual
reasoning, particularly spatial
reasoning, is a challenging cognitive task that requires understanding object relationships and their interactions within complex environments, especially in robotics domain. Existing
vision_
language models (VLMs) excel at perception tasks but struggle with fine-grained spatial
reasoning due to their implicit, correlation-driven
reasoning and reliance solely on images. We propose a novel neuro_symbolic framework that integrates both panoramic-image and 3D point cloud information, combining neural perception with symbolic
reasoning to explicitly model spatial and logical relationships. Our framework consists of a perception module for detecting entities and extracting attributes, and a
reasoning module that constructs a structured scene graph to support precise, interpretable queries. Evaluated on the JRDB-
Reasoning dataset, our approach demonstrates superior performance and reliability in crowded, human_built environments while maintaining a lightweight design suitable for robotics and
embodied AI applications.
Read the full abstract ▾
All authors Simindokht Jahangard, Mehrzad Mohammadi, Abhinav Dhall, Hamid Rezatofighi
Robotics
A Hermetic, Transparent Soft Growing Vine Robot System for Pipe Inspection
William E. Heap, Yimeng Qin, Kai Hammond et al. (+3 more)
Rehabilitation of aging pipes requires accurate condition assessment and mapping far into the pipe interiors. Soft growing vine
robot systems are particularly promising for navigating confined, sinuous paths such as in pipes, but are currently limited by complex subsystems and a lack of validation in real-world industrial settings. In this paper, we introduce the concept and implementation of a hermetic and transparent vine
robot system for visual condition assessment and mapping within non-branching pipes. This design encloses all mechanical and electrical components within the vine
robot's soft, airtight, and transparent body, protecting them from environmental interference while enabling visual sensing. Because this approach requires an enclosed mechanism for transporting sensors, we developed, modeled, and tested a passively adapting enclosed tip mount. Finally, we validated the hermetic and transparent vine
robot system concept through a real-world condition assessment and mapping task in a wastewater pipe. This work advances the use of soft-growing vine
robots in pipe inspection by developing and demonstrating a robust, streamlined, field-validated system suitable for continued development and deployment.
Read the full abstract ▾
All authors William E. Heap, Yimeng Qin, Kai Hammond, Anish Bayya, Haonon Kong, Allison M. Okamura
Robotics · Cross-listed from eess.SY
Cooperative Integrated Estimation-Guidance for Simultaneous Interception of Moving Targets
Lohitvel Gopikannan, Shashi Ranjan Kumar and Abhinav Sinha
This paper proposes a cooperative integrated estimation-guidance framework for simultaneous interception of a non-maneuvering target using a team of unmanned autonomous vehicles, assuming only a subset of vehicles are equipped with dedicated sensors to measure the target's states. Unlike earlier approaches that focus solely on either estimation or guidance design, the proposed framework unifies both within a cooperative architecture. To circumvent the limitation posed by heterogeneity in target observability, sensorless vehicles estimate the target's state by leveraging information exchanged with neighboring
agents over a directed communication
topology through a prescribed-time observer. The proposed approach employs true proportional
navigation guidance (TPNG), which uses an exact time-to-go formulation and is applicable across a wide spectrum of target motions. Furthermore, prescribed-time observer and controller are employed to achieve convergence to true target's state and consensus in time-to-go within set predefined times, respectively. Simulations demonstrate the effectiveness of the proposed framework under various engagement scenarios.
Read the full abstract ▾
Robotics
RepV: Safety-Separable Latent Spaces for Scalable Neurosymbolic Plan Verification
Yunhao Yang, Neel P. Bhatt, Pranay Samineni et al. (+3 more)
As AI systems migrate to safety-critical domains, verifying that their actions comply with well-defined rules remains a challenge. Formal methods provide provable guarantees but demand hand-crafted temporal-logic specifications, offering limited expressiveness and accessibility. Deep learning approaches enable evaluation of plans against natural-
language constraints, yet their opaque decision process invites misclassifications with potentially severe consequences. We introduce RepV, a neurosymbolic verifier that unifies both views by learning a latent space where safe and unsafe plans are linearly separable. Starting from a modest seed set of plans labeled by an off-the-shelf model checker, RepV trains a lightweight projector that embeds each plan, together with a
language model-generated rationale, into a low-dimensional space; a frozen linear boundary then verifies compliance for unseen natural-
language rules in a single forward pass. Beyond binary classification, RepV provides a probabilistic guarantee on the likelihood of correct verification based on its position in the latent space. This guarantee enables a guarantee-driven refinement of the planner, improving rule compliance without human annotations. Empirical evaluations show that RepV improves compliance prediction accuracy by up to 15% compared to baseline methods while adding fewer than 0.2M parameters. Furthermore, our refinement framework outperforms ordinary fine-tuning baselines across various planning domains. These results show that safety-separable latent spaces offer a scalable, plug-and-play primitive for reliable neurosymbolic plan verification. Code and data are available at: https://repv-project.github.io/.
Read the full abstract ▾
All authors Yunhao Yang, Neel P. Bhatt, Pranay Samineni, Rohan Siva, Zhanyang Wang, Ufuk Topcu
Robotics
Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence
Zachary Ravichandran, Fernando Cladera, Ankit Prabhu et al. (+6 more)
While heterogeneous teams have typically been designed for well-specified missions with known semantics, generative intelligence, i.e., large
language models (LLMs) and
vision language models (VLMs), opens the possibility of teams that infer mission-relevant semantics and subtasks given high-level natural
language specifications and environmental context. However, current LLM- and VLM-enabled teaming methods typically assume well-structured and known environments, limiting performance in complex real-world settings. We address these limitations via SPINE-HT, a framework that grounds the
reasoning abilities of LLMs in the evolving context of a heterogeneous
robot team through a three-stage process. Given mission specifications and team capabilities in natural
language, an LLM infers necessary subtasks. These subtasks are validated for feasibility and then assigned to
robots based on capabilities such as traversability or perception. Finally, the framework uses feedback (semantic maps and task outcomes) acquired by the
robots to update subtasks online. In simulation experiments with closed-loop perception and control, our framework achieves nearly twice the success rate compared to prior LLM-enabled heterogeneous teaming approaches. In real-world experiments with a Clearpath Jackal, a Clearpath Husky, a Boston Dynamics Spot, and a high-altitude
UAV, our method achieves an 87.5% success rate in missions requiring
reasoning about
robot capabilities and refining subtasks with online feedback. More information is provided at: https://zacravichandran.github.io/SPINE-HT.
Read the full abstract ▾
All authors Zachary Ravichandran, Fernando Cladera, Ankit Prabhu, Jason Hughes, Carlos Nieto-Granda, Varun Murali, Camillo Taylor, George J. Pappas, Vijay Kumar