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…

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…

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…

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

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.

This paper addresses the vulnerability of DNNs used in robotic semantic segmentation to adversarial attacks by proposing specialized detection strategies to enhance safety in robotic perception system…

This paper introduces Chronos, a physics-informed framework for non-Markovian long-horizon manipulation, which elevates observation history to the latent state of the policy dynamics and achieves high…

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…

The paper proposes a novel method to identify parsimonious explicit piece-wise polynomial relationships, demonstrating its effectiveness in modeling the inverse kinematics of industrial manipulator ro…

This paper presents a method to equip existing motion planning algorithms with probabilistic task-completion guarantees on systems with unknown dynamics using a planner-agnostic constraint-tightening…

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

This paper proposes a data-driven framework for contact force estimation in Deformable Tool Manipulation using a compact LSTM and few-shot adaptation strategy.

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 proposes a sensorless four-channel architecture for teleoperation using inverse dynamics modeling, outperforming conventional methods in human-scale manipulation tasks.

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

The paper proposes a coherent inverse reinforcement learning (IRL) method to improve large behavior models for robotic control, achieving superior sample efficiency and performance on complex sparse m…

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.