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20232026
most citedLEGATO: Cross-Embodiment Imitation Using a Grasping Tool

7 citations · 8 across the 11 of their papers we have counts for

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13 papers · 1 filter

cs.RO2026

Catch Me If You Can: Real-Time Feedback Denoising for Responsive VLAs

Yiheng Ji, Xingru Zhou, Luis Sentis +1

Vision-Language-Action (VLA) models have shown strong generalization in robotic manipulation by combining semantic knowledge from pretrained vision-language models with expressive…

cs.RO2026

LUMO: Designing Luminous Contact Morphology for Repeatable Whole-Finger Contact Observation

Dong Ho Kang, Youngsu Ko, Luis Sentis

A low-impedance robot finger reports through joint torque how strongly it is loaded, but the same torque can arise from a small force near the fingertip or a large force near the j…

cs.RO2026

Whole-Body Planning for Humanoids Navigating Confined Spaces via Self-Collision Avoidance References

Carlos Gonzalez, Luis Sentis

Humanoid locomotion in highly confined environments requires navigating dense environmental obstacles and complex self-collision bounds while maintaining multi-contact dynamic feas…

cs.RO2026

A Bilateral Teleoperation Framework for Dexterous Manipulation

Stefano Dalla Gasperina, Dong Ho Kang, Haiyun Zhang +10

Dexterous teleoperation requires precise arm-hand coordination, low-latency feedback, and robust interaction in real-world contact-rich environments. This paper presents a modular…

cs.RO2026

ARISTO Hand: Sensing-Driven Distal Hyperextension for Fine-Grained Manipulation

Aaron Kim, Dong Ho Kang, Mark Helwig +4

Manipulating thin objects requires precise contact geometry and reliable force perception, yet many anthropomorphic robotic hands lack the mechanical and sensing capabilities neede…

cs.RO2026

PLATO Hand: Shaping Contact Behavior with Fingernails for Precise Manipulation

Dong Ho Kang, Aaron Kim, Mingyo Seo +3

We present the PLATO Hand, a dexterous robotic hand with a hybrid fingertip that combines a rigid fingernail, embedded distal phalanx, and compliant pulp to shape contact behavior…

cs.RO2025

HARMONIC: A Content-Centric Cognitive Robotic Architecture

Sanjay Oruganti, Sergei Nirenburg, Marjorie McShane +6

This paper introduces HARMONIC, a cognitive-robotic architecture designed for robots in human-robotic teams. HARMONIC supports semantic perception interpretation, human-like decisi…

cs.RO2024★ 7 cited

LEGATO: Cross-Embodiment Imitation Using a Grasping Tool

Mingyo Seo, H. Andy Park, Shenli Yuan +2

Cross-embodiment imitation learning enables policies trained on specific embodiments to transfer across different robots, unlocking the potential for large-scale imitation learning…

cs.RO2024

Guiding Collision-Free Humanoid Multi-Contact Locomotion using Convex Kinematic Relaxations and Dynamic Optimization

Carlos Gonzalez, Luis Sentis

Humanoid robots rely on multi-contact planners to navigate a diverse set of environments, including those that are unstructured and highly constrained. To synthesize stable multi-c…

cs.RO2024

RPC: A Modular Framework for Robot Planning, Control, and Deployment

Seung Hyeon Bang, Carlos Gonzalez, Gabriel Moore +3

This paper presents an open-source, lightweight, yet comprehensive software framework, named RPC, which integrates physics-based simulators, planning and control libraries, debuggi…

cs.RO2024

RL-augmented MPC Framework for Agile and Robust Bipedal Footstep Locomotion Planning and Control

Seung Hyeon Bang, Carlos Arribalzaga Jové, Luis Sentis

This paper proposes an online bipedal footstep planning strategy that combines model predictive control (MPC) and reinforcement learning (RL) to achieve agile and robust bipedal ma…

cs.RO2024

Variable Inertia Model Predictive Control for Fast Bipedal Maneuvers

Seung Hyeon Bang, Jaemin Lee, Carlos Gonzalez +1

This paper proposes a novel control framework for agile and robust bipedal locomotion, addressing model discrepancies between full-body and reduced-order models. Specifically, assu…

cs.RO2023★ 1 cited

Deep Imitation Learning for Humanoid Loco-manipulation through Human Teleoperation

Mingyo Seo, Steve Han, Kyutae Sim +4

We tackle the problem of developing humanoid loco-manipulation skills with deep imitation learning. The difficulty of collecting task demonstrations and training policies for human…