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CARD: Contact-Anchored Retargeting for Dexterous Manipulation
Cunxi Dai, Botao He, Francois R Hogan +7
Humans are dexterous, but traditional data collection paradigms such as teleoperation, kinesthetic teaching, and wearable exoskeletons force humans to act through the robot embodim…
EmbodyHOI: Embodiment-Aware Generation of Hand-Object Interactions for Humanoid Dexterous Manipulation
Chaitanya Chawla, Kartik Sharma, Ananya Bal +2
Humanoid dexterous manipulation needs reference motions that are both semantically rich and kinematically feasible for the target robot. Text-conditioned hand-object interaction (H…
Memory Without Curation: Learning Multi-Stream Latent Memory for Robot Policies
Yilin Wu, Rutav Shah, Shuaiyi Huang +5
It is widely appreciated that robotic policies require long-term memory to perform complex tasks. However, directly conditioning policies on all past observations leaves policy lea…
PanTact: Teaching Whole-Body Dexterity with a Wearable Contact Interface
Xiaomeng Xu, Han Zhang, Zhian Ruan +6
Humanoid robots have many degrees of freedom and contact surfaces, yet most loco-manipulation research restricts manipulation to the hands and avoids other body contacts with the e…
Fly-by-Code: Embodied Coding Agents for Aerial Manipulation with Active Visual and Physical Feedback
Jaewoo Lee, Jeongyeon Seo, Sihyun Cho +10
Embodied coding agents construct robot policies from natural-language instructions by generating executable programs and refining them from execution feedback. This refinement assu…
Trajectory Planning without Trajectory Data: A Manifold-Guided Approach
Silong Yong, Anji Liu, Cunxi Dai +5
A common way for trajectory planning is to leverage generative models trained on large collections of expert trajectories. At inference time, the model generates executable traject…
SimEX: Simulation-Integrated Robotics AutoResearch
Jiaheng Hu, Roberto Martin-Martin, Peter Stone +3
Coding agents powered by large language models (LLMs) have shown remarkable abilities to autonomously reason about and achieve goals in the digital world. However, bringing this su…
Counterfactual Video Generation Enables Scalable Humanoid Loco-Manipulation
Zihan Wang, Zhen Wu, Pieter Abbeel +6
Teaching humanoids loco-manipulation skills, such as carrying diverse objects, via visual imitation is a promising path toward generalist robots. However, collecting diverse, high-…
Humanoid Safe Stop via Learned Stoppability Value
Junfeng Long, Pieter Abbeel, Koushil Sreenath +3
Humanoid robots responding to emergency stop commands typically execute a fixed maneuver, without reasoning about whether a safe stop is actually feasible from the current state. W…
Worlds in One Demo: A Synthetic Data Engine for Learning Open-World Mobile Manipulation
Lingxiao Guo, Huanyu Li, Guanya Shi
Learning open-world mobile manipulation policies requires vast data to achieve spatial generalization, long-horizon robustness, and scene generalization. Current prevailing data co…
ASPIRE: Agentic /Skills Discovery for Robotics
Runyu Lu, Yubo Wu, Ethan Kou +13
Traditional robot programming is notoriously challenging: it requires orchestrating multimodal perception, managing physical contact dynamics, and handling diverse environment conf…
VLK: Learning Humanoid Loco-Manipulation from Synthetic Interactions in Reconstructed Scenes
Yen-Jen Wang, Jiaman Li, Sirui Chen +9
Perception-based humanoid loco-manipulation requires connecting egocentric observations and task instructions to whole-body motion. Learning this mapping requires synchronized egoc…
FADA: Few-Shot Domain Adaptation via Dynamics Alignment for Humanoid Control
Angchen Xie, Nikhil Sobanbabu, Ishayu Shikhare +3
High-precision humanoid control is limited by target-domain dynamics mismatch, where the same control objective can induce different realized motions under changes in terrain, payl…
SceneBot: Contact-Prompted General Humanoid Whole Body Tracking with Scene-Interaction
Sirui Chen, Shibo Zhao, Zhen Wu +3
Current humanoid reinforcement-learning policies excel at free-space motions but struggle with contact-rich tasks, as pure kinematic tracking cannot resolve the physical ambiguitie…
Scalable Behavior Cloning with Open Data, Training, and Evaluation
Arthur Allshire, Himanshu Gaurav Singh, Ritvik Singh +15
We introduce ABC, a fully open-source stack for manipulation with behavior cloning. At its core is ABC-130K: the largest open-source teleoperation dataset to date, featuring 3,500…
Mana: Dexterous Manipulation of Articulated Tools
Zhao-Heng Yin, Guanya Shi, Pieter Abbeel +1
Articulated tool manipulation remains a major challenge in dexterous robotics due to the need to coordinate internal degrees of freedom and contact-rich interactions. While prior w…
LUCID: Learning Embodiment-Agnostic Intent Models from Unstructured Human Videos for Scalable Dexterous Robot Skill Acquisition
Harsh Gupta, Guanya Shi, Wenzhen Yuan
The most widely-adopted robot learning pipelines today learn skills from robot demonstrations or structured human data, which are expensive to collect and tied to specific embodime…
LadderMan: Learning Humanoid Perceptive Ladder Climbing
Siheng Zhao, Yuanhang Zhang, Ziqi Lu +6
Humanoid robots hold great promise for operating in human-centered environments, yet ladder climbing remains one of the most challenging tasks due to sparse footholds and handholds…
RIO: Flexible Real-Time Robot I/O for Cross-Embodiment Robot Learning
Pablo Ortega-Kral, Eliot Xing, Arthur Bucker +13
Despite recent efforts to collect multi-task, multi-embodiment datasets, to design recipes for training Vision-Language-Action models (VLAs), and to showcase these models on differ…
Autonomous Integration and Improvement of Robotic Assembly using Skill Graph Representations
Peiqi Yu, Philip Huang, Chaitanya Chawla +3
Robotic assembly systems traditionally require substantial manual engineering effort to integrate new tasks, adapt to new environments, and improve performance over time. This pape…
CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
Letian Fu, Justin Yu, Karim El-Refai +13
"Code-as-Policy" considers how executable code can complement data-intensive Vision-Language-Action (VLA) methods, yet their effectiveness as autonomous controllers for embodied ma…
Flow Policy Gradients for Robot Control
Brent Yi, Hongsuk Choi, Himanshu Gaurav Singh +9
Likelihood-based policy gradient methods are the dominant approach for training robot control policies from rewards. These methods rely on differentiable action likelihoods, which…
Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching
Zhen Wu, Xiaoyu Huang, Lujie Yang +8
While recent advances in humanoid locomotion have achieved stable walking on varied terrains, capturing the agility and adaptivity of highly dynamic human motions remains an open c…
RPL: Learning Robust Humanoid Perceptive Locomotion on Challenging Terrains
Yuanhang Zhang, Younggyo Seo, Juyue Chen +7
Humanoid perceptive locomotion has made significant progress and shows great promise, yet achieving robust multi-directional locomotion on complex terrains remains underexplored. T…