4 citations · 9 across the 21 of their papers we have counts for
16 papers · 1 filter
Continual Humanoid Motion Learning
Zhewen He, Hao Huang, Geeta Chandra Raju Bethala +4
Humanoid whole-body controllers can now track a diverse set of dynamic motions, but they are typically trained offline and then frozen, so teaching such a controller a new skill te…
Keep the Effect, Drop the Actor: Programmable Effect-to-Execution World-Action Models
Junyi Hu, Zhewen He, Zhenhua Li +1
A robot demonstration records two things in the same frames: what happened to the objects, and how one particular arm made it happen. We condition on the first. A demonstration is…
Learning Adaptive Safety Margins for Visual Navigation
Junyi Hu, Shuaihang Yuan, Geeta Chandra Raju Bethala +2
Robots in cluttered indoor spaces often fail not because they cannot generate collision-free paths, but because a fixed safety margin is mis-calibrated: conservative margins cause…
Immersive Social Interaction with VR and LLM-Assisted Humanoids
Niraj Pudasaini, Geeta Chandra Raju Bethala, Pranav Doma +2
Humanoid robots can extend human presence to remote, constrained, or hazardous environments, but existing teleoperation interfaces often require physically demanding motion trackin…
One-shot Adaptation of Humanoid Whole-body Motion with Walking Priors
Hao Huang, Geeta Chandra Raju Bethala, Shuaihang Yuan +4
Whole-body humanoid motion represents a fundamental challenge in robotics, requiring balance, coordination, and adaptability to enable human-like behaviors. However, existing metho…
Wavelet Policy: Lifting Scheme for Policy Learning in Long-Horizon Tasks
Hao Huang, Shuaihang Yuan, Geeta Chandra Raju Bethala +3
Policy learning focuses on devising strategies for agents in embodied artificial intelligence systems to perform optimal actions based on their perceived states. One of the key cha…
FastMap: Real-Time Semantic Map Completion via Bitwise Masked Modeling
Yijie Deng, Shuaihang Yuan, Congcong Wen +4
Semantic map completion, which predicts the layout of unobserved regions from partial observations, is a critical capability for indoor robot navigation. Existing approaches either…
H2-COMPACT: Human-Humanoid Co-Manipulation via Adaptive Contact Trajectory Policies
Geeta Chandra Raju Bethala, Hao Huang, Niraj Pudasaini +5
We present a hierarchical policy-learning framework that enables a legged humanoid to cooperatively carry extended loads with a human partner using only haptic cues for intent infe…
Humanoid Agent via Embodied Chain-of-Action Reasoning with Multimodal Foundation Models for Zero-Shot Loco-Manipulation
Congcong Wen, Geeta Chandra Raju Bethala, Yu Hao +8
Humanoid loco-manipulation, which integrates whole-body locomotion with dexterous manipulation, remains a fundamental challenge in robotics. Beyond whole-body coordination and bala…
Integrating Retrospective Framework in Multi-Robot Collaboration
Jiazhao Liang, Hao Huang, Yu Hao +4
Recent advancements in Large Language Models (LLMs) have demonstrated substantial capabilities in enhancing communication and coordination in multi-robot systems. However, existing…
GAMap: Zero-Shot Object Goal Navigation with Multi-Scale Geometric-Affordance Guidance
Shuaihang Yuan, Hao Huang, Yu Hao +3
Zero-Shot Object Goal Navigation (ZS-OGN) enables robots or agents to navigate toward objects of unseen categories without object-specific training. Traditional approaches often le…
Reliable Semantic Understanding for Real World Zero-shot Object Goal Navigation
Halil Utku Unlu, Shuaihang Yuan, Congcong Wen +3
We introduce an innovative approach to advancing semantic understanding in zero-shot object goal navigation (ZS-OGN), enhancing the autonomy of robots in unfamiliar environments. T…
Exploring the Reliability of Foundation Model-Based Frontier Selection in Zero-Shot Object Goal Navigation
Shuaihang Yuan, Halil Utku Unlu, Hao Huang +3
In this paper, we present a novel method for reliable frontier selection in Zero-Shot Object Goal Navigation (ZS-OGN), enhancing robotic navigation systems with foundation models t…
Zero-shot Object Navigation with Vision-Language Models Reasoning
Congcong Wen, Yisiyuan Huang, Hao Huang +6
Object navigation is crucial for robots, but traditional methods require substantial training data and cannot be generalized to unknown environments. Zero-shot object navigation (Z…
MultiTalk: Introspective and Extrospective Dialogue for Human-Environment-LLM Alignment
Venkata Naren Devarakonda, Ali Umut Kaypak, Shuaihang Yuan +3
LLMs have shown promising results in task planning due to their strong natural language understanding and reasoning capabilities. However, issues such as hallucinations, ambiguitie…
How Secure Are Large Language Models (LLMs) for Navigation in Urban Environments?
Congcong Wen, Jiazhao Liang, Shuaihang Yuan +6
In the field of robotics and automation, navigation systems based on Large Language Models (LLMs) have recently demonstrated impressive performance. However, the security aspects o…