activity
20172023
most citedLearning to be Safe: Deep RL with a Safety Critic

26 citations · 62 across the 25 of their papers we have counts for

collaborators

37 papers

cs.RO2023

Transforming a Quadruped into a Guide Robot for the Visually Impaired: Formalizing Wayfinding, Interaction Modeling, and Safety Mechanism

J. Taery Kim, Wenhao Yu, Yash Kothari +3

This paper explores the principles for transforming a quadrupedal robot into a guide robot for individuals with visual impairments. A guide robot has great potential to resolve the…

cs.RO2023★ 16 cited

Principles and Guidelines for Evaluating Social Robot Navigation Algorithms

Anthony Francis, Claudia Pérez-D'Arpino, Chengshu Li +28

A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation. While the field of social navigation ha…

cs.RO2023

ACE: Adversarial Correspondence Embedding for Cross Morphology Motion Retargeting from Human to Nonhuman Characters

Tianyu Li, Jungdam Won, Alexander Clegg +3

Motion retargeting is a promising approach for generating natural and compelling animations for nonhuman characters. However, it is challenging to translate human movements into se…

cs.RO2023

Learning and Adapting Agile Locomotion Skills by Transferring Experience

Laura Smith, J. Chase Kew, Tianyu Li +5

Legged robots have enormous potential in their range of capabilities, from navigating unstructured terrains to high-speed running. However, designing robust controllers for highly…

cs.RO2023★ 2 cited

ASC: Adaptive Skill Coordination for Robotic Mobile Manipulation

Naoki Yokoyama, Alex Clegg, Joanne Truong +6

We present Adaptive Skill Coordination (ASC) -- an approach for accomplishing long-horizon tasks like mobile pick-and-place (i.e., navigating to an object, picking it, navigating t…

cs.RO2023★ 1 cited

ARMP: Autoregressive Motion Planning for Quadruped Locomotion and Navigation in Complex Indoor Environments

Jeonghwan Kim, Tianyu Li, Sehoon Ha

Generating natural and physically feasible motions for legged robots has been a challenging problem due to its complex dynamics. In this work, we introduce a novel learning-based f…