3 citations · 6 across the 14 of their papers we have counts for
17 papers
Workspace-Bench 1.0: Benchmarking AI Agents on Workspace Tasks with Large-Scale File Dependencies
Zirui Tang, Xuanhe Zhou, Yumou Liu +19
Workspace learning requires AI agents to identify, reason over, exploit, and update explicit and implicit dependencies among heterogeneous files in a worker's workspace, enabling t…
PointMapPolicy: Structured Point Cloud Processing for Multi-Modal Imitation Learning
Xiaogang Jia, Qian Wang, Anrui Wang +12
Robotic manipulation systems benefit from complementary sensing modalities, where each provides unique environmental information. Point clouds capture detailed geometric structure,…
FLOWER: Democratizing Generalist Robot Policies with Efficient Vision-Language-Action Flow Policies
Moritz Reuss, Hongyi Zhou, Marcel Rühle +3
Developing efficient Vision-Language-Action (VLA) policies is crucial for practical robotics deployment, yet current approaches face prohibitive computational costs and resource re…
MoRe-ERL: Learning Motion Residuals using Episodic Reinforcement Learning
Xi Huang, Hongyi Zhou, Ge Li +5
We propose MoRe-ERL, a framework that combines Episodic Reinforcement Learning (ERL) and residual learning, which refines preplanned reference trajectories into safe, feasible, and…
BEAST: Efficient Tokenization of B-Splines Encoded Action Sequences for Imitation Learning
Hongyi Zhou, Weiran Liao, Xi Huang +11
We present the B-spline Encoded Action Sequence Tokenizer (BEAST), a novel action tokenizer that encodes action sequences into compact discrete or continuous tokens using B-splines…
MonoMobility: Zero-Shot 3D Mobility Analysis from Monocular Videos
Hongyi Zhou, Yulan Guo, Xiaogang Wang +1
Accurately analyzing the motion parts and their motion attributes in dynamic environments is crucial for advancing key areas such as embodied intelligence. Addressing the limitatio…
NAMI: Efficient Image Generation via Bridged Progressive Rectified Flow Transformers
Yuhang Ma, Bo Cheng, Shanyuan Liu +4
Flow-based Transformer models have achieved state-of-the-art image generation performance, but often suffer from high inference latency and computational cost due to their large pa…
Chunking the Critic: A Transformer-based Soft Actor-Critic with N-Step Returns
Dong Tian, Onur Celik, Gerhard Neumann
We introduce a sequence-conditioned critic for Soft Actor-Critic (SAC) that models trajectory context with a lightweight Transformer and trains on aggregated -step targets. Unli…
X-IL: Exploring the Design Space of Imitation Learning Policies
Xiaogang Jia, Atalay Donat, Xi Huang +8
Designing modern imitation learning (IL) policies requires making numerous decisions, including the selection of feature encoding, architecture, policy representation, and more. As…
Towards Fusing Point Cloud and Visual Representations for Imitation Learning
Atalay Donat, Xiaogang Jia, Xi Huang +7
Learning for manipulation requires using policies that have access to rich sensory information such as point clouds or RGB images. Point clouds efficiently capture geometric struct…
IRIS: An Immersive Robot Interaction System
Xinkai Jiang, Qihao Yuan, Enes Ulas Dincer +11
This paper introduces IRIS, an Immersive Robot Interaction System leveraging Extended Reality (XR). Existing XR-based systems enable efficient data collection but are often challen…
BMP: Bridging the Gap between B-Spline and Movement Primitives
Weiran Liao, Ge Li, Hongyi Zhou +2
This work introduces B-spline Movement Primitives (BMPs), a new Movement Primitive (MP) variant that leverages B-splines for motion representation. B-splines are a well-known conce…
A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics
Puze Liu, Jonas Günster, Niklas Funk +17
Machine learning methods have a groundbreaking impact in many application domains, but their application on real robotic platforms is still limited. Despite the many challenges ass…
TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning
Ge Li, Dong Tian, Hongyi Zhou +3
This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policie…
Variational Distillation of Diffusion Policies into Mixture of Experts
Hongyi Zhou, Denis Blessing, Ge Li +4
This work introduces Variational Diffusion Distillation (VDD), a novel method that distills denoising diffusion policies into Mixtures of Experts (MoE) through variational inferenc…
MaIL: Improving Imitation Learning with Mamba
Xiaogang Jia, Qian Wang, Atalay Donat +7
This work presents Mamba Imitation Learning (MaIL), a novel imitation learning (IL) architecture that provides an alternative to state-of-the-art (SoTA) Transformer-based policies.…
Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning
Ge Li, Hongyi Zhou, Dominik Roth +4
Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods e…