Updated on 2026.06.11

Usage instructions: here

CoRL 2025

Keyword	Title & Abstract	Authors	Links
tactile	Reactive In-Air Clothing Manipulation with Confidence-Aware Dense Correspondence and Visuotactile Affordance Manipulating clothing is challenging due to their complex, variable configurations and frequent self-occlusion. While prior systems often rely on flattening garments, humans routinely identify keypoints in highly crumpled and suspended states. We present a novel, task-agnostic, visuotactile framework that operates directly on crumpled clothing—including in-air configurations that have not been…	Alberto Rodriguez Garcia Team	OpenReview
tactile	Cross-Sensor Touch Generation Today’s visuo-tactile sensors come in many shapes and sizes, making it challenging to develop general-purpose tactile representations. This is because most models are tied to a specific sensor design. To address this challenge, we propose two approaches to cross-sensor image generation. The first is an end-to-end method that leverages paired data (Touch2Touch). The second method builds an…	Nima Fazeli Team	OpenReview
sim2real	Non-conflicting Energy Minimization in Reinforcement Learning based Robot Control Efficient robot locomotion often requires balancing task performance with energy expenditure. A common approach in reinforcement learning (RL) is to penalize energy use directly in the reward function. This requires carefully weighting the reward terms to avoid undesirable trade-offs where energy minimization harms task success or vice versa. In this work, we propose a hyperparameter-free…	Stefan Lee Team	OpenReview
sim2real	Learning Impact-Rich Rotational Maneuvers via Centroidal Velocity Rewards and Sim-to-Real Techniques: A One-Leg Hopper Flip Case Study Dynamic rotational maneuvers, such as front flips, inherently involve large angular momentum generation and intense impact forces, presenting major challenges for reinforcement learning and sim-to-real transfer. In this work, we propose a general framework for learning and deploying impact-rich, rotation-intensive behaviors through centroidal velocity-based rewards and actuator-aware sim-to-real…	Hae-Won Park Team	OpenReview
sim2real	Sampling-based System Identification with Active Exploration for Legged Sim2Real Learning Sim-to-real discrepancies hinder learning-based policies from achieving high-precision tasks in the real world. While Domain Randomization (DR) is commonly used to bridge this gap, it often relies on heuristics and can lead to overly conservative policies with degrading performance when not properly tuned. System Identification (Sys-ID) offers a targeted approach, but standard techniques rely on…	Guanya Shi Team	OpenReview
tactile sim2real	Text2Touch: Tactile In-Hand Manipulation with LLM-Designed Reward Functions Large language models (LLMs) are beginning to automate reward design for dexterous manipulation. However, no prior work has considered tactile sensing, which is known to be critical for human-like dexterity. We present Text2Touch, bringing LLM-crafted rewards to the challenging task of multi-axis in-hand object rotation with real-world vision based tactile sensing in palm-up and palm-down…	Nathan F. Lepora Team	OpenReview
learnedcontrol	Versatile Loco-Manipulation through Flexible Interlimb Coordination The ability to flexibly leverage limbs for loco-manipulation is essential for enabling autonomous robots to operate in unstructured environments. Yet, prior work on loco-manipulation is often constrained to specific tasks or predetermined limb configurations. In this work, we present einforcement Learning for Interlimb Coordination (ReLIC), an approach that enables versatile loco-manipulation…	Kuan Fang Team	OpenReview
tactile sim2real	SimShear: Sim-to-Real Shear-based Tactile Servoing We present SimShear: a sim-to-real pipeline for tactile control that allows use of shear information without explicitly modeling shear dynamics in simulation. Shear, which arises from lateral movements across contact surfaces, are critical for tasks involving dynamic object interactions but are challenging to simulate. We introduce shPix2pix: a shear-conditioned U-Net GAN that transforms…	Nathan F. Lepora Team	OpenReview
sim2real learnedcontrol	Embrace Contacts: humanoid shadowing with full body ground contacts Previous humanoid robot research works treat the robot as a bipedal mobile manipulation platform, where only the feet and hands contact the environment. However, we humans use all body parts to interact with the world, e.g., we sit in chairs, get up from the ground, or roll on the floor. Contacting the environment using body parts other than feet and hands brings significant challenges in both…	Hang Zhao Team	OpenReview
sim2real	Decentralized Aerial Manipulation of a Cable-Suspended Load Using Multi-Agent Reinforcement Learning This paper presents the first decentralized method to enable real-world 6-DoF manipulation of a cable-suspended load using a team of Micro-Aerial Vehicles (MAVs). Our method leverages multi-agent reinforcement learning (MARL) to train an outer-loop control policy for each MAV. Unlike state-of-the-art controllers that utilize a centralized scheme, our policy does not require global states,…	Sihao Sun Team	OpenReview
learnedcontrol	Hand-Eye Autonomous Delivery: Learning Humanoid Navigation, Locomotion and Reaching We propose Hand-Eye Autonomous Delivery (HEAD), a framework that learns navigation, locomotion, and reaching skills for humanoids, directly from human motion and vision perception data. We take a modular approach where the high-level planner commands the target position and orientation of the hands and eyes of the humanoid, delivered by the low-level policy that controls the whole-body movements….	Karen Liu Team	OpenReview
sim2real learnedcontrol	Visual Imitation Enables Contextual Humanoid Control How can we teach humanoids to climb staircases and sit on chairs using the surrounding environment context? Arguably the simplest way is to just show them—casually capture a human motion video and feed it to humanoids. We introduce VideoMimic, a real-to-sim-to-real pipeline that mines everyday videos, jointly reconstructs the humans and the environment, and produces whole-body control…	Angjoo Kanazawa Team	OpenReview
learnedcontrol	CLONE: Closed-Loop Whole-Body Humanoid Teleoperation for Long-Horizon Tasks Humanoid robot teleoperation plays a vital role in demonstrating and collecting data for complex interactions. Current methods suffer from two key limitations: (1) restricted controllability due to decoupled upper- and lower-body control, and (2) severe drift caused by open-loop execution. These issues prevent humanoid robots from performing coordinated whole-body motions required for…	Siyuan Huang Team	OpenReview
sim2real	X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real Human videos offer a scalable way to train robot manipulation policies, but lack the action labels needed by standard imitation learning algorithms. Existing cross-embodiment approaches try to map human motion to robot actions, but often fail when the embodiments differ significantly. We propose X-Sim, a real-to-sim-to-real framework that uses object motion as a dense and transferable signal for…	Sanjiban Choudhury Team	OpenReview
sim2real	ClutterDexGrasp: A Sim-to-Real System for General Dexterous Grasping in Cluttered Scenes Dexterous grasping in cluttered scenes presents significant challenges due to diverse object geometries, occlusions, and potential collisions. Existing methods primarily focus on single-object grasping or grasp-pose prediction without interaction, which are insufficient for complex, cluttered scenes. Recent vision-language-action models offer a potential solution but require extensive real-world…	Hao Dong Team	OpenReview
sim2real	Disentangled Multi-Context Meta-Learning: Unlocking Robust and Generalized Task Learning In meta-learning and its downstream tasks, many methods use implicit adaptation to represent task-specific variations. However, implicit approaches hinder interpretability and make it difficult to understand which task factors drive performance. In this work, we introduce a disentangled multi-context meta-learning framework that explicitly learns separate context vectors for different aspects…	Seongil Hong Team	OpenReview
sim2real	Wheeled Lab: Modern Sim2Real for Low-cost, Open-source Wheeled Robotics Simulation has been pivotal in recent robotics milestones and is poised to play a prominent role in the field’s future. However, recent robotic advances often rely on expensive and high-maintenance platforms, limiting access to broader robotics audiences. This work introduces Wheeled Lab, a framework for integrating the low-cost, open-source wheeled platforms that are already widely established…	Byron Boots Team	OpenReview
learnedcontrol	BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities Real-world household tasks present significant challenges for mobile manipulation robots. An analysis of existing robotics benchmarks reveals that successful task performance hinges on three key whole-body control capabilities: bimanual coordination, stable and precise navigation, and extensive end-effector reachability. Achieving these capabilities requires careful hardware design, but the…	Li Fei-Fei Team	OpenReview
learnedcontrol	Multi-critic Learning for Whole-body End-effector Twist Tracking Learning whole-body control for locomotion and arm motions in a single policy has challenges, as the two tasks have conflicting goals. For instance, efficient locomotion typically favors a horizontal base orientation, while end-effector tracking may benefit from base tilting to extend reachability. Additionally, current Reinforcement Learning (RL) approaches using a pose-based task specification…	Marco Hutter Team	OpenReview
sim2real	JaxRobotarium: Training and Deploying Multi-Robot Policies in 10 Minutes Multi-agent reinforcement learning (MARL) has emerged as a promising solution for learning complex and scalable coordination behaviors in multi-robot systems. However, established MARL platforms (e.g., SMAC and MPE) lack robotics relevance and hardware deployment, leaving multi-robot learning researchers to develop bespoke environments and hardware testbeds dedicated to the development and…	Harish Ravichandar Team	OpenReview
sim2real	ATK: Automatic Task-driven Keypoint Selection for Robust Policy Learning Learning visuamotor policy through imitation learning often suffers from perceptual challenges, where visual differences between training and evaluation environments degrade policy performance. Policies relying on state estimations like 6D pose, require task-specific tracking and are difficult to scale, while raw sensor-based policies may lack robustness to small visual disturbances. In this…	Abhishek Gupta Team	OpenReview
tactile	LocoTouch: Learning Dynamic Quadrupedal Transport with Tactile Sensing Quadrupedal robots have demonstrated remarkable agility and robustness in traversing complex terrains. However, they struggle with dynamic object interactions, where contact must be precisely sensed and controlled. To bridge this gap, we present LocoTouch, a system that equips quadrupedal robots with tactile sensing to address a particularly challenging task in this category: long-distance…	Ding Zhao Team	OpenReview
sim2real	WoMAP: World Models For Embodied Open-Vocabulary Object Localization Active object localization remains a critical challenge for robots, requiring efficient exploration of partially observable environments. However, state-of-the-art robot policies either struggle to generalize beyond demonstration datasets (e.g., imitation learning methods) or fail to generate physically grounded actions (e.g., VLMs). To address these limitations, we introduce WoMAP (World Models…	Lihan Zha Team	OpenReview
sim2real	Learning Deployable Locomotion Control via Differentiable Simulation Differentiable simulators promise to improve sample efficiency in robot learning by providing analytic gradients of the system dynamics. Yet, their application to contact-rich tasks like locomotion is complicated by the inherently non-smooth nature of contact, impeding effective gradient-based optimization. Existing works thus often rely on soft contact models that provide smooth gradients but…	Marco Hutter Team	OpenReview
sim2real	HuB: Learning Extreme Humanoid Balance The human body demonstrates exceptional motor capabilities—such as standing steadily on one foot or performing a high kick with the leg raised over 1.5 meters—both requiring precise balance control. While recent research on humanoid control has leveraged reinforcement learning to track human motions for skill acquisition, applying this paradigm to balance-intensive tasks remains challenging. In…	Yang Gao Team	OpenReview
tactile	DexSkin: High-Coverage Conformable Robotic Skin for Learning Contact-Rich Manipulation Human skin provides a rich tactile sensing stream, localizing intentional and unintentional contact events over a large and contoured region. Replicating these tactile sensing capabilities for dexterous robotic manipulation systems remains a longstanding challenge. In this work, we take a step towards this goal by introducing DexSkin. DexSkin is a soft, conformable capacitive electronic skin that…	Jiajun Wu Team	OpenReview
learnedcontrol	Granular loco-manipulation: Repositioning rocks through strategic sand avalanche Legged robots have the potential to leverage obstacles to climb steep sand slopes. However, efficiently repositioning these obstacles to desired locations is challenging. Here we present DiffusiveGRAIN, a learning-based method that enables a multi-legged robot to strategically induce localized sand avalanches during locomotion and indirectly manipulate obstacles. We conducted 375 trials,…	Feifei Qian Team	OpenReview
sim2real	Lucid-XR: An Extended-Reality Data Engine for Robotic Manipulation We introduce Lucid-XR, a generative data engine for creating diverse and realistic-looking data to train real-world robot systems. At the core of Lucid-XR is vuer, a web-based physics simulation environment that runs directly on the XR headset, enabling internet-scale access to immersive, latency-free virtual interactions without requiring specialized equipment. The complete system integrates…	Ge Yang Team	OpenReview
sim2real	GraspVLA: a Grasping Foundation Model Pre-trained on Billion-scale Synthetic Action Data Embodied foundation models are gaining increasing attention for their zero-shot generalization, scalability, and adaptability to new tasks through few-shot post-training. However, existing models rely heavily on real-world data, which is costly and labor-intensive to collect. Synthetic data offers a cost-effective alternative, yet its potential remains largely underexplored. To bridge this gap,…	He Wang Team	OpenReview
sim2real	ControlVLA: Few-shot Object-centric Adaptation for Pre-trained Vision-Language-Action Models Learning real-world robotic manipulation is challenging, particularly when limited demonstrations are available. Existing methods for few-shot manipulation often rely on simulation-augmented data or pre-built modules like grasping and pose estimation, which struggle with sim-to-real gaps and lack extensibility. While large-scale imitation pre-training shows promise, adapting these general-purpose…	Siyuan Huang Team	OpenReview
learnedcontrol	Steering Your Diffusion Policy with Latent Space Reinforcement Learning Robotic control policies learned from human demonstrations have achieved impressive results in many real-world applications. However, in scenarios where initial performance is not satisfactory, as is often the case in novel open-world settings, such behavioral cloning (BC)-learned policies typically require collecting additional human demonstrations to further improve their behavior—an…	Sergey Levine Team	OpenReview
tactile	UniTac2Pose: A Unified Approach Learned in Simulation for Category-level Visuotactile In-hand Pose Estimation Accurate estimation of the in-hand pose of an object based on its CAD model is crucial in both industrial applications and everyday tasks—ranging from positioning workpieces and assembling components to seamlessly inserting devices like USB connectors. While existing methods often rely on regression, feature matching, or registration techniques, achieving high precision and generalizability to…	Hao Dong Team	OpenReview
sim2real	FetchBot: Learning Generalizable Object Fetching in Cluttered Scenes via Zero-Shot Sim2Real Generalizable object fetching in cluttered scenes remains a fundamental and application-critical challenge in embodied AI. Closely packed objects cause inevitable occlusions, making safe action generation particularly difficult. Under such partial observability, effective policies must not only generalize across diverse objects and layouts but also reason about occlusion to avoid collisions….	He Wang Team	OpenReview
learnedcontrol	FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots Reinforcement learning (RL) has made significant strides in legged robot control, enabling locomotion across diverse terrains and complex loco-manipulation capabilities. However, the commonly used position or velocity tracking-based objectives are agnostic to forces experienced by the robot, leading to stiff and potentially dangerous behaviors and poor control during forceful interactions. To…	Huazhe Xu Team	OpenReview
tactile	VT-Refine: Learning Bimanual Assembly with Visuo-Tactile Feedback via Simulation Fine-Tuning Humans excel at bimanual assembly tasks by adapting to rich tactile feedback—a capability that remains difficult to replicate in robots through behavioral cloning alone, due to the suboptimality and limited diversity of human demonstrations. In this work, we present VT-Refine, a visuo-tactile policy learning framework that combines real-world demonstrations, high-fidelity tactile simulation, and…	Yunzhu Li Team	OpenReview
tactile	Self-supervised perception for tactile skin covered dexterous hands We present PercepSkin, a pre-trained encoder for magnetic skin sensors distributed across the fingertips, phalanges, and palm of a dexterous robot hand. Magnetic tactile skins offer a flexible form factor for hand-wide coverage with fast response times, in contrast to vision-based tactile sensors that are restricted to the fingertips and limited by bandwidth. Full hand tactile perception is…	Mustafa Mukadam Team	OpenReview
learnedcontrol	Learning a Unified Policy for Position and Force Control in Legged Loco-Manipulation Robotic loco-manipulation tasks often involve contact-rich interactions with the environment, requiring the joint modeling of contact force and robot position. However, recent visuomotor policies often focus solely on position or force control, overlooking their integration. In this work, we propose a unified policy for legged robots that jointly models force and position control learned without…	Siyuan Huang Team	OpenReview
learnedcontrol	Humanoid Policy ~ Human Policy Training manipulation policies for humanoid robots with diverse data enhances their robustness and generalization across tasks and platforms. However, learning solely from robot demonstrations is labor-intensive, requiring expensive tele-operated data collection,n which is difficult to scale. This paper investigates a more scalable data source, egocentric human demonstrations, to serve as…	Xiaolong Wang Team	OpenReview
learnedcontrol	ToddlerBot: Open-Source ML-Compatible Humanoid Platform for Loco-Manipulation Learning-based robotics research driven by data demands a new approach to robot hardware design—one that serves as both a platform for policy execution and a tool for embodied data collection. We introduce ToddlerBot, a low-cost, open-source humanoid robot platform designed for robotics and AI research. ToddlerBot enables seamless acquisition of high-quality simulation and real-world data. The…	Karen Liu Team	OpenReview
learnedcontrol	Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids Learning generalizable robot manipulation policies, especially for complex multi-fingered humanoids, remains a significant challenge. Existing approaches primarily rely on extensive data collection and imitation learning, which are expensive, labor-intensive, and difficult to scale. Sim-to-real reinforcement learning (RL) offers a promising alternative, but has mostly succeeded in simpler…	Yuke Zhu Team	OpenReview
learnedcontrol	TWIST: Teleoperated Whole-Body Imitation System Teleoperating humanoid robots in a whole-body manner marks a fundamental step toward developing general-purpose robotic intelligence, with human motion providing an ideal interface for controlling all degrees of freedom. Yet, most current humanoid teleoperation systems fall short of enabling coordinated whole-body behavior, typically limiting themselves to isolated locomotion or manipulation…	Karen Liu Team	OpenReview
tactile	exUMI: Extensible Robot Teaching System with Action-aware Task-agnostic Tactile Representation Tactile-aware robot learning faces critical challenges in data collection and representation due to data scarcity and sparsity, and the absence of force feedback in existing systems. To address these limitations, we introduce a tactile robot learning system with both hardware and algorithm innovations. We present exUMI, an extensible data collection device that enhances the vanilla UMI with…	Yong-Lu Li Team	OpenReview