6 citations · 6 across the 9 of their papers we have counts for
6 papers · 1 filter
What Should Embodied Agents Remember? Benchmarking and Evaluating Memory Modality for Embodied Agents
Junxiang Wang, Yuchen Liu, Yi Wu +6
Memory allows large language model (LLM) agents to use past experience, often stored as textual records, when responding to requests. For embodied agents that control robots, visua…
BiRoAD: Learning Shared and Role-Adaptive Representations for Bimanual Manipulation
Yan Shen, Yuchen Liu, Feng Jiang +5
Bimanual manipulation requires policies that coordinate two arms while adapting their functional roles to scene geometry, object configuration, and task context. Learning such scen…
Traj-LeWM: Path-Aware World-Model Planning via Latent Trajectory Cost
Xiaodi Huang, Ziyi Ding, Jingtian Wan +6
LeWM is a lightweight visual world model that learns latent dynamics end-to-end from pixels and ranks candidate action sequences by the distance between their predicted endpoints a…
RoboWM-Bench: A Benchmark for Evaluating World Models in Robotic Manipulation
Feng Jiang, Yang Chen, Kyle Xu +8
Recent advances in large-scale video world models have enabled increasingly realistic future prediction, raising the prospect of using generated videos as scalable supervision for…
M-VQA: A Benchmark for Multimodal, Multi-Entity, Multi-Hop Visual Question Answering
Jiatong Ma, Longteng Guo, Yuchen Liu +4
We present M-VQA, a novel knowledge-based Visual Question Answering (VQA) benchmark, to enhance the evaluation of multimodal large language models (MLLMs) in fine-grained multi…
BiPreManip: Learning Affordance-Based Bimanual Preparatory Manipulation through Anticipatory Collaboration
Yan Shen, Feng Jiang, Zichen He +5
Many everyday objects are difficult to directly grasp (e.g., a flat iPad) or manipulate functionally (e.g., opening the cap of a pen lying on a desk). Such tasks require sequential…