5 citations · 7 across the 7 of their papers we have counts for
7 papers
VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents
Zirui Wang, Junyi Zhang, Jiaxin Ge +9
Modern Vision-Language Models (VLMs) remain poorly characterized in multi-step visual interactions, particularly in how they integrate perception, memory, and action over long hori…
LAVQA: A Latency-Aware Visual Question Answering Framework for Shared Autonomy in Self-Driving Vehicles
Shuangyu Xie, Kaiyuan Chen, Wenjing Chen +5
When uncertainty is high, self-driving vehicles may halt for safety and benefit from the access to remote human operators who can provide high-level guidance. This paradigm, known…
Robo-DM: Data Management For Large Robot Datasets
Kaiyuan Chen, Letian Fu, David Huang +9
Recent results suggest that very large datasets of teleoperated robot demonstrations can be used to train transformer-based models that have the potential to generalize to new scen…
Specifications: The missing link to making the development of LLM systems an engineering discipline
Ion Stoica, Matei Zaharia, Joseph Gonzalez +8
Despite the significant strides made by generative AI in just a few short years, its future progress is constrained by the challenge of building modular and robust systems. This ca…
DiffusionSeeder: Seeding Motion Optimization with Diffusion for Rapid Motion Planning
Huang Huang, Balakumar Sundaralingam, Arsalan Mousavian +3
Running optimization across many parallel seeds leveraging GPU compute have relaxed the need for a good initialization, but this can fail if the problem is highly non-convex as all…
Mechanical Search on Shelves with Efficient Stacking and Destacking of Objects
Huang Huang, Letian Fu, Michael Danielczuk +6
Stacking increases storage efficiency in shelves, but the lack of visibility and accessibility makes the mechanical search problem of revealing and extracting target objects diffic…
MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance
Michael Luo, Ashwin Balakrishna, Brijen Thananjeyan +6
Safe exploration is critical for using reinforcement learning (RL) in risk-sensitive environments. Recent work learns risk measures which measure the probability of violating const…