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arXiv2024

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation

Language-conditioned robotic learning has significantly enhanced robot adaptability by enabling a single model to execute diverse tasks in response to verbal commands. Despite these advancements, security vulnerabilities within this domain remain largely unexplored. This paper addresses this gap by proposing a novel adversarial prompt attack tailored to language-conditioned robotic models. Our approach involves crafting a universal adversarial prefix that induces the model to perform unintended actions when added to any original prompt. We demonstrate that existing adversarial techniques exhibit limited effectiveness when directly transferred to the robotic domain due to the inherent robustness of discretized robotic action spaces. To overcome this challenge, we propose to optimize adversarial prefixes based on continuous action representations, circumventing the discretization process. Additionally, we identify the beneficial impact of intermediate features on adversarial attacks and leverage the negative gradient of intermediate self-attention features to further enhance attack efficacy. Extensive experiments on VIMA models across 13 robot manipulation tasks validate the superiority of our method over existing approaches and demonstrate its transferability across different model variants.

cs.LG↗

arXiv2025

LuciBot: Automated Robot Policy Learning from Generated Videos

Automatically generating training supervision for embodied tasks is crucial, as manual designing is tedious and not scalable. While prior works use large language models (LLMs) or vision-language models (VLMs) to generate rewards, these approaches are largely limited to simple tasks with well-defined rewards, such as pick-and-place. This limitation arises because LLMs struggle to interpret complex scenes compressed into text or code due to their restricted input modality, while VLM-based rewards, though better at visual perception, remain limited by their less expressive output modality. To address these challenges, we leverage the imagination capability of general-purpose video generation models. Given an initial simulation frame and a textual task description, the video generation model produces a video demonstrating task completion with correct semantics. We then extract rich supervisory signals from the generated video, including 6D object pose sequences, 2D segmentations, and estimated depth, to facilitate task learning in simulation. Our approach significantly improves supervision quality for complex embodied tasks, enabling large-scale training in simulators.

cs.CV↗

arXiv2025

Re-purposing a modular origami manipulator into an adaptive physical computer for machine learning and robotic perception

Physical computing has emerged as a powerful tool for performing intelligent tasks directly in the mechanical domain of functional materials and robots, reducing our reliance on the more traditional COMS computers. However, no systematic study explains how mechanical design can influence physical computing performance. This study sheds insights into this question by repurposing an origami-inspired modular robotic manipulator into an adaptive physical reservoir and systematically evaluating its computing capacity with different physical configurations, input setups, and computing tasks. By challenging this adaptive reservoir computer to complete the classical NARMA benchmark tasks, this study shows that its time series emulation performance directly correlates to the Peak Similarity Index (PSI), which quantifies the frequency spectrum correlation between the target output and reservoir dynamics. The adaptive reservoir also demonstrates perception capabilities, accurately extracting its payload weight and orientation information from the intrinsic dynamics. Importantly, such information extraction capability can be measured by the spatial correlation between nodal dynamics within the reservoir body. Finally, by integrating shape memory alloy (SMA) actuation, this study demonstrates how to exploit such computing power embodied in the physical body for practical, robotic operations. This study provides a strategic framework for harvesting computing power from soft robots and functional materials, demonstrating how design parameters and input selection can be configured based on computing task requirements. Extending this framework to bio-inspired adaptive materials, prosthetics, and self-adaptive soft robotic systems could enable next-generation embodied intelligence, where the physical structure can compute and interact with their digital counterparts.

cs.RO↗

arXiv2026

EquiContact: A Hierarchical SE(3) Vision-to-Force Equivariant Framework for Spatially Generalizable Contact-rich Tasks

This paper presents a framework for learning vision-based robotic policies for contact-rich manipulation tasks, which generalize spatially across task configurations. We propose EquiContact, a novel hierarchical framework composed of a high-level vision planner, such as a Diffusion Equivariant Descriptor Field (Diff-EDF) and a low-level compliant visuomotor policy, such as a Geometric Compliant Action Chunking Transformer (G-CompACT). G-CompACT, operating using localized observations from wrist-mounted RGB images, end-effector pose feedback in the form of a geometrically consistent error vector (GCEV) and force-torque readings, produces actions defined on the end-effector frame. We show that the entire EquiContact pipeline is $\SE$-equivariant under equivariant reference-frame estimation and left-invariant visual features, from perception to force control. Once the robot learns to execute a contact-rich task, such as inserting a peg-in-hole (PiH), it can execute similar tasks at different locations and orientations without retraining of the low-level policy. We also describe three key components for attaining spatially generalizable contact-rich policies: geometrically consistent compliance, the use of localized policies, and induced equivariance. Real-world peg-in-hole (PiH) experiments demonstrate near-perfect success rates under spatially out-of-distribution configurations, validating the proposed framework and principles, while additional screwing and surface-wiping experiments evaluate the transfer of the localized policy design beyond insertion. The experimental videos and more details can be found on the project website: https://equicontact.github.io/EquiContact-website/

cs.RO↗

arXiv2026

Learning Surgical Robotic Manipulation with 3D Spatial Priors

Achieving 3D spatial awareness is crucial for surgical robotic manipulation, where precise and delicate operations are required. Existing methods either explicitly reconstruct the surgical scene prior to manipulation, or enhance multi-view features by adding wrist-mounted cameras to supplement the default stereo endoscopes. However, both paradigms suffer from notable limitations: the former easily leads to error accumulation and prevents end-to-end optimization due to its multi-stage nature, while the latter is rarely adopted in clinical practice since wrist-mounted cameras can interfere with the motion of surgical robot arms. In this work, we introduce the Spatial Surgical Transformer (SST), an end-to-end visuomotor policy that empowers surgical robots with 3D spatial awareness by directly exploring 3D spatial cues embedded in endoscopic images. First, we build Surgical3D, a large-scale photorealistic dataset containing 30K stereo endoscopic image pairs with accurate 3D geometry, addressing the scarcity of 3D data in surgical scenes. Based on Surgical3D, we finetune a powerful geometric transformer to extract robust 3D latent representations from stereo endoscopes images. These representations are then seamlessly aligned with the robot's action space via a lightweight multi-level spatial feature connector (MSFC), all within an endoscope-centric coordinate frame. Extensive real-robot experiments demonstrate that SST achieves state-of-the-art performance and strong spatial generalization on complex surgical tasks such as knot tying and ex-vivo organ dissection, representing a significant step toward practical clinical deployment. The dataset and code will be released.

cs.RO↗

arXiv2026

Efficient Camera Pose Augmentation for View Generalization in Robotic Policy Learning

Prevailing 2D-centric visuomotor policies exhibit a pronounced deficiency in novel view generalization, as their reliance on static observations hinders consistent action mapping across unseen views. In response, we introduce GenSplat, a feed-forward 3D Gaussian Splatting framework that facilitates view-generalized policy learning through novel view rendering. GenSplat employs a permutation-equivariant architecture to reconstruct high-fidelity 3D scenes from sparse, uncalibrated inputs in a single forward pass. To ensure structural integrity, we design a 3D-prior distillation strategy that regularizes the 3DGS optimization, preventing the geometric collapse typical of purely photometric supervision. By rendering diverse synthetic views from these stable 3D representations, we systematically augment the observational manifold during training. This augmentation forces the policy to ground its decisions in underlying 3D structures, thereby ensuring robust execution under severe spatial perturbations where baselines severely degrade.

cs.RO↗

arXiv2026

ActionMap: Robot Policy Learning via Voxel Action Heatmap

Vision-language-action (VLA) models have advanced rapidly across backbones, training recipes, and data scale, yet the action decoder, which converts the backbone's hidden state into a continuous control signal, has barely changed and remains a single-point predictor across the majority of current VLAs. Whether implemented via autoregressive token bins, L1 regression, or flow-matching denoising, the resulting decoder treats the action space as unstructured, leaving the geometric proximity of neighboring actions unexploited during training. To advance this, we introduce ActionMap, a voxel heatmap action head that drops into an existing VLA in place of its native action decoder. For each new action, the head predicts a voxel heatmap over the action space, where each voxel directly stores the probability of the corresponding action. Across LIBERO simulation and real-world Franka manipulation, our heatmap head surpasses two architecturally distinct backbones at matched training steps (e.g., +8.2% over OpenVLA-OFT's L1 regression head on the LIBERO four-suite average), converges at comparable or faster rates on both backbones, and remains markedly more data-efficient at low training data. The cross-backbone consistency indicates that action representation is a real lever for VLA performance, distinct from further backbone or recipe scaling. Project Page: https://showlab.github.io/ActionMap/.

cs.RO↗

arXiv2026

UMI-Live for In-The-Wild Bimanual Robotics Data Collection

Universal Manipulation Interface (UMI) devices allow human experts to demonstrate tasks for robot learning without teleoperating physical robots. Using these demonstrations as training data still requires a consistent hardware setup, calibration, and data processing. We present UMI-Live, a full-stack open-source bimanual UMI system for in-the-wild data collection. We design our custom UMI-Live hardware to integrate camera and LiDAR--inertial sensing, onboard compute, and power, without external tracking equipment or calibration within the collection environment. We implement a simple user interface for recording data while an asynchronous pipeline performs parallel SLAM reconstruction and publishes training-ready datasets. We also release software that supports dual-YAM teleoperation and VLA policy training and inference. We validate UMI-Live data quality by fine-tuning $π_{0.5}$ and MolmoAct2, achieving success rates of 10/10 and 8/10, respectively on a bimanual pick-and-place task. Furthermore, we release 40 hours of UMI-Live data across three tasks, and complete hardware designs, software, and reproduction guides at our $\href{https://umi-live.github.io/}{\text{project website}}$.

cs.RO↗

arXiv2023

SEER: Safe Efficient Exploration for Aerial Robots using Learning to Predict Information Gain

We address the problem of efficient 3-D exploration in indoor environments for micro aerial vehicles with limited sensing capabilities and payload/power constraints. We develop an indoor exploration framework that uses learning to predict the occupancy of unseen areas, extracts semantic features, samples viewpoints to predict information gains for different exploration goals, and plans informative trajectories to enable safe and smart exploration. Extensive experimentation in simulated and real-world environments shows the proposed approach outperforms the state-of-the-art exploration framework by 24% in terms of the total path length in a structured indoor environment and with a higher success rate during exploration.

cs.RO↗

arXiv2023

Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment

In this paper, a novel robotic grasping system is established to automatically pick up objects in cluttered scenes. A composite robotic hand composed of a suction cup and a gripper is designed for grasping the object stably. The suction cup is used for lifting the object from the clutter first and the gripper for grasping the object accordingly. We utilize the affordance map to provide pixel-wise lifting point candidates for the suction cup. To obtain a good affordance map, the active exploration mechanism is introduced to the system. An effective metric is designed to calculate the reward for the current affordance map, and a deep Q-Network (DQN) is employed to guide the robotic hand to actively explore the environment until the generated affordance map is suitable for grasping. Experimental results have demonstrated that the proposed robotic grasping system is able to greatly increase the success rate of the robotic grasping in cluttered scenes.

cs.RO↗

arXiv2019

Deep Learning Based Robot for Automatically Picking up Garbage on the Grass

This paper presents a novel garbage pickup robot which operates on the grass. The robot is able to detect the garbage accurately and autonomously by using a deep neural network for garbage recognition. In addition, with the ground segmentation using a deep neural network, a novel navigation strategy is proposed to guide the robot to move around. With the garbage recognition and automatic navigation functions, the robot can clean garbage on the ground in places like parks or schools efficiently and autonomously. Experimental results show that the garbage recognition accuracy can reach as high as 95%, and even without path planning, the navigation strategy can reach almost the same cleaning efficiency with traditional methods. Thus, the proposed robot can serve as a good assistance to relieve dustman's physical labor on garbage cleaning tasks.

cs.CV↗

arXiv2015

Learning Mobile Robot Based on Adaptive Controlled Markov Chains

Herein we suggest a mobile robot-training algorithm that is based on the preference approximation of the decision taker who controls the robot, which in its turn is managed by the Markov chain. Setup of the model parameters is made on the basis of the data referring to the situations and decisions involving the decision taker. The model that adapts to the decision taker's preferences can be set up either a priori, during the process of the robot's normal operation, or during specially planned testing sessions. Basing on the simulation modelling data of the robot's operation process and on the decision taker's robot control we have set up the model parameters thus illustrating both working capacity of all algorithm components and adaptation effectiveness.

cs.RO↗

arXiv2019

How to enhance learning of robotic surgery gestures? A tactile cue saliency investigation for 3D hand guidance

The current generation of surgeons requires extensive training in teleoperation to develop specific dexterous skills, which are independent of medical knowledge. Training curricula progress from manipulation tasks to simulated surgical tasks but are limited in time. To tackle this, we propose to integrate surgical robotic training together with Haptic Feedback (HF) to improve skill acquisition. This paper present the initial but promising results of our haptic device designed to support in the training of surgical gestures. Our ongoing work is related to integrate the HF in the RAVEN II platform.

cs.RO↗

arXiv2025

DoublyAware: Dual Planning and Policy Awareness for Temporal Difference Learning in Humanoid Locomotion

Achieving robust robot learning for humanoid locomotion is a fundamental challenge in model-based reinforcement learning (MBRL), where environmental stochasticity and randomness can hinder efficient exploration and learning stability. The environmental, so-called aleatoric, uncertainty can be amplified in high-dimensional action spaces with complex contact dynamics, and further entangled with epistemic uncertainty in the models during learning phases. In this work, we propose DoublyAware, an uncertainty-aware extension of Temporal Difference Model Predictive Control (TD-MPC) that explicitly decomposes uncertainty into two disjoint interpretable components, i.e., planning and policy uncertainties. To handle the planning uncertainty, DoublyAware employs conformal prediction to filter candidate trajectories using quantile-calibrated risk bounds, ensuring statistical consistency and robustness against stochastic dynamics. Meanwhile, policy rollouts are leveraged as structured informative priors to support the learning phase with Group-Relative Policy Constraint (GRPC) optimizers that impose a group-based adaptive trust-region in the latent action space. This principled combination enables the robot agent to prioritize high-confidence, high-reward behavior while maintaining effective, targeted exploration under uncertainty. Evaluated on the HumanoidBench locomotion suite with the Unitree 26-DoF H1-2 humanoid, DoublyAware demonstrates improved sample efficiency, accelerated convergence, and enhanced motion feasibility compared to RL baselines. Our simulation results emphasize the significance of structured uncertainty modeling for data-efficient and reliable decision-making in TD-MPC-based humanoid locomotion learning.

cs.RO↗

arXiv2016

Learning Dynamic Robot-to-Human Object Handover from Human Feedback

Object handover is a basic, but essential capability for robots interacting with humans in many applications, e.g., caring for the elderly and assisting workers in manufacturing workshops. It appears deceptively simple, as humans perform object handover almost flawlessly. The success of humans, however, belies the complexity of object handover as collaborative physical interaction between two agents with limited communication. This paper presents a learning algorithm for dynamic object handover, for example, when a robot hands over water bottles to marathon runners passing by the water station. We formulate the problem as contextual policy search, in which the robot learns object handover by interacting with the human. A key challenge here is to learn the latent reward of the handover task under noisy human feedback. Preliminary experiments show that the robot learns to hand over a water bottle naturally and that it adapts to the dynamics of human motion. One challenge for the future is to combine the model-free learning algorithm with a model-based planning approach and enable the robot to adapt over human preferences and object characteristics, such as shape, weight, and surface texture.

cs.RO↗

arXiv2026

KiRAS: Keyframe Guided Self-Imitation for Robust and Adaptive Skill Learning in Quadruped Robots

With advances in reinforcement learning and imitation learning, quadruped robots can acquire diverse skills within a single policy by imitating multiple skill-specific datasets. However, the lack of datasets on complex terrains limits the ability of such multi-skill policies to generalize effectively in unstructured environments. Inspired by animation, we adopt keyframes as minimal and universal skill representations, relaxing dataset constraints and enabling the integration of terrain adaptability with skill diversity. We propose Keyframe Guided Self-Imitation for Robust and Adaptive Skill Learning (KiRAS), an end-to-end framework for acquiring and transitioning between diverse skill primitives on complex terrains. KiRAS first learns diverse skills on flat terrain through keyframe-guided self-imitation, eliminating the need for expert datasets; then continues training the same policy network on rough terrains to enhance robustness. To eliminate catastrophic forgetting, a proficiency-based Skill Initialization Technique is introduced. Experiments on Solo-8 and Unitree Go1 robots show that KiRAS enables robust skill acquisition and smooth transitions across challenging terrains. This framework demonstrates its potential as a lightweight platform for multi-skill generation and dataset collection. It further enables flexible skill transitions that enhance locomotion on challenging terrains.

cs.RO↗

arXiv2021

Learning multiple gaits of quadruped robot using hierarchical reinforcement learning

There is a growing interest in learning a velocity command tracking controller of quadruped robot using reinforcement learning due to its robustness and scalability. However, a single policy, trained end-to-end, usually shows a single gait regardless of the command velocity. This could be a suboptimal solution considering the existence of optimal gait according to the velocity for quadruped animals. In this work, we propose a hierarchical controller for quadruped robot that could generate multiple gaits (i.e. pace, trot, bound) while tracking velocity command. Our controller is composed of two policies, each working as a central pattern generator and local feedback controller, and trained with hierarchical reinforcement learning. Experiment results show 1) the existence of optimal gait for specific velocity range 2) the efficiency of our hierarchical controller compared to a controller composed of a single policy, which usually shows a single gait. Codes are publicly available.

cs.RO↗

arXiv2023

Toward Human-Like Social Robot Navigation: A Large-Scale, Multi-Modal, Social Human Navigation Dataset

Humans are well-adept at navigating public spaces shared with others, where current autonomous mobile robots still struggle: while safely and efficiently reaching their goals, humans communicate their intentions and conform to unwritten social norms on a daily basis; conversely, robots become clumsy in those daily social scenarios, getting stuck in dense crowds, surprising nearby pedestrians, or even causing collisions. While recent research on robot learning has shown promises in data-driven social robot navigation, good-quality training data is still difficult to acquire through either trial and error or expert demonstrations. In this work, we propose to utilize the body of rich, widely available, social human navigation data in many natural human-inhabited public spaces for robots to learn similar, human-like, socially compliant navigation behaviors. To be specific, we design an open-source egocentric data collection sensor suite wearable by walking humans to provide multi-modal robot perception data; we collect a large-scale (~100 km, 20 hours, 300 trials, 13 humans) dataset in a variety of public spaces which contain numerous natural social navigation interactions; we analyze our dataset, demonstrate its usability, and point out future research directions and use cases.

cs.RO↗