GSAM introduces a generalizable and safe robotic framework for articulated object manipulation, significantly improving success rates and reducing variability across diverse tasks by integrating commo…

This paper presents Mana, a sim-to-real framework for dexterous articulated tool manipulation.

This paper presents a robot-agnostic compliant-control framework with standardized interfaces for ROS control ecosystem, enabling reusable infrastructure for various manipulators and diverse compliant…

This paper presents a data-driven method to estimate external joint torques without dedicated force sensors, enabling force-feedback teleoperation on low-cost arms.

The paper proposes an iCEM+TL framework that combines the Sample-efficient Cross-Entropy Method with Transfer Learning and Reward Redesign to improve robotic motion planning for complex tasks like sta…

This paper proposes coupled egocentric control, a body-following teleoperation approach for whole-body robot control, improving efficiency, reducing effort, and increasing ease of use.

The paper proposes an automated data-collection scheme for contact-rich tasks using offline deep reinforcement learning, achieving high success rates and generalization.

This paper organizes embodied data sources for multimodal foundation models into a pyramid, focusing on real-robot, UMI-style, egocentric and exocentric, simulation, and general vision-language data.

A two-layer framework using a large language model for force-conditioned reinforce learning with recovery maneuvers and force signatures.

The paper introduces RoboTrustBench, a comprehensive benchmark that evaluates the trustworthiness of video world models for robotic manipulation across challenging scenarios, finding that current mode…

The paper introduces Center-of-Pressure (CoP), a physics-grounded tactile representation that enables robust zero-shot sim-to-real transfer for complex, contact-rich manipulation tasks.

The paper proposes CTRL-STEER, a closed-loop framework that adaptively adjusts intervention strength to stabilize concept regulation and improve task success in Vision-Language-Action models without r…

A new framework called HOST enables robots to acquire new skills from a single human video in seconds while retaining previously mastered skills.

The paper proposes FT-WBC, a fault-tolerant loco-manipulation framework for legged manipulators using a decoupled policy architecture, a Fault Estimator, and a Posture Adaptation Module for fault-awar…

Development of a compliant full-body telepresence control stack for miniature humanoids, enabling tele-locomotion and manipulation.