6 citations · 8 across the 18 of their papers we have counts for
23 papers
What Matters in Quality-Aware VLA Pretraining: A Benchmark and Empirical Study
Mingxuan Yan, Litian Gong, Dihong Huang +19
How should demonstration quality be used during vision-language-action (VLA) pretraining to improve performance after target-task fine-tuning? We compare weighting, filtering, and…
CoSkill: Whole-Body Control via Skill Composition for Long-Horizon Human-Environment Interaction
Jeonghwan Kim, Hyeonwoo Kim, Hanbyul Joo
Achieving human-level dexterity in complex, unstructured environments requires the seamless integration of whole-body scene interaction and dexterous object manipulation skills. Wh…
One from Infinity: Actualizing Futures from Pretrained World Models into Robot Actions
Bang Du, Yichen Xie, Shuqi Zhao +3
A pretrained video world model admits many plausible futures for a scene, but a robot must realize the exact task-conditioned one. To turn world models into executable robot polici…
RopeFormer: Cross-Trial Adaptation from Interaction History for Dynamic Rope Manipulation
Menglin Wu, Kaixiang Yao, Shangbo Luan +2
Dynamic rope manipulation is highly sensitive to unknown object dynamics: the same robot motion can produce substantially different responses across ropes, while explicitly identif…
CARF: Contrastive Attraction-Repulsion of Failure-Guided Flow Matching
Shuqi Zhao, Bang Du, Cheng-En Wu +3
Robot demonstration collection often produces imperfect or failed trajectories in addition to successful demonstrations. Existing methods typically exploit failed trajectories by i…
TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model
Anqi Li, Yuxin Chen, Zhaobo Li +4
We study the problem of navigating cluttered indoor environments with a humanoid robot. Unlike conventional methods that model navigation as a 2D path planning problem, humanoid tr…
3DWay: Generalizing Robot Manipulation via 3D Consistent Waypoints
Ziqin Huang, Yingyue Li, Chenyangguang Zhang +6
Intermediate representations are key to bridging the modality gap between generalizable manipulation policies and large-scale pretrained vision-language models (VLMs). Among these,…
Diagnosing Compositional Generalization in Sequential Robot Tasks
Yixiao Wang, Cheng-En Wu, Lingfeng Sun +5
Sequential robot manipulation requires policies to execute novel combinations of familiar instruction components. However, collecting demonstrations for all possible instruction tu…
CLIFT: Turning Gemini Robotics On-Device into Humanoid Specialists via Non-Invasive Closed-Loop Iterative Fine-Tuning
Yuxin Chen, Hari Srikanth, Nathan Jew +7
While robot foundation models are growing increasingly capable, the strongest models are typically trained on proprietary data and remain closed-source, limiting downstream users'…
RoboDojo: A Unified Sim-and-Real Benchmark for Comprehensive Evaluation of Generalist Robot Manipulation Policies
Tianxing Chen, Yue Chen, Zixuan Li +41
Generalist robot manipulation policies have advanced rapidly, yet existing benchmarks remain limited in systematically evaluating their capabilities. Many rely on simple, short-hor…
REAR: Test-time Preference Realignment through Reward Decomposition
Fuxiang Zhang, Pengcheng Wang, Chenran Li +6
Aligning large language models (LLMs) with diverse user preferences is a critical yet challenging task. While post-training methods can adapt models to specific needs, they often r…
Mean Flow Policy with Instantaneous Velocity Constraint for One-step Action Generation
Guojian Zhan, Letian Tao, Pengcheng Wang +6
Learning expressive and efficient policy functions is a promising direction in reinforcement learning (RL). While flow-based policies have recently proven effective in modeling com…
DADP: Domain Adaptive Diffusion Policy
Pengcheng Wang, Qinghang Liu, Haotian Lin +4
Learning domain adaptive policies that can generalize to unseen transition dynamics, remains a fundamental challenge in learning-based control. Substantial progress has been made t…
Bootstrap Off-policy with World Model
Guojian Zhan, Likun Wang, Xiangteng Zhang +3
Online planning has proven effective in reinforcement learning (RL) for improving sample efficiency and final performance. However, using planning for environment interaction inevi…
Mind Your Entropy: From Maximum Entropy to Trajectory Entropy-Constrained RL
Guojian Zhan, Likun Wang, Pengcheng Wang +4
Maximum entropy has become a mainstream off-policy reinforcement learning (RL) framework for balancing exploitation and exploration. However, two bottlenecks still limit further pe…
Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control
Yuxin Chen, Jianglan Wei, Chenfeng Xu +4
World models enable robots to "imagine" future observations given current observations and planned actions, and have been increasingly adopted as generalized dynamics models to fac…
GeoManip: Geometric Constraints as General Interfaces for Robot Manipulation
Weiliang Tang, Jia-Hui Pan, Yun-Hui Liu +4
We present GeoManip, a framework to enable generalist robots to leverage essential conditions derived from object and part relationships, as geometric constraints, for robot manipu…
RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
Charles Xu, Qiyang Li, Jianlan Luo +1
Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, th…
Maximizing Alignment with Minimal Feedback: Efficiently Learning Rewards for Visuomotor Robot Policy Alignment
Ran Tian, Yilin Wu, Chenfeng Xu +3
Visuomotor robot policies, increasingly pre-trained on large-scale datasets, promise significant advancements across robotics domains. However, aligning these policies with end-use…
RoVi-Aug: Robot and Viewpoint Augmentation for Cross-Embodiment Robot Learning
Lawrence Yunliang Chen, Chenfeng Xu, Karthik Dharmarajan +6
Scaling up robot learning requires large and diverse datasets, and how to efficiently reuse collected data and transfer policies to new embodiments remains an open question. Emergi…
Efficient Reinforcement Learning of Task Planners for Robotic Palletization through Iterative Action Masking Learning
Zheng Wu, Yichuan Li, Wei Zhan +3
The development of robotic systems for palletization in logistics scenarios is of paramount importance, addressing critical efficiency and precision demands in supply chain managem…
The Feasibility Theory of Constrained Reinforcement Learning: A Tutorial Study
Yujie Yang, Zhilong Zheng, Masayoshi Tomizuka +2
Satisfying safety constraints is a priority concern when solving optimal control problems (OCPs). Due to the existence of infeasibility phenomenon, where a constraint-satisfying so…
DBPF: A Framework for Efficient and Robust Dynamic Bin-Picking
Yichuan Li, Junkai Zhao, Yixiao Li +4
Efficiency and reliability are critical in robotic bin-picking as they directly impact the productivity of automated industrial processes. However, traditional approaches, demandin…