1 citations · 1 across the 9 of their papers we have counts for
12 papers
Red-Teaming of Vision-Language-Action Models via Quality Diversity Environment Generation
Shihan Zhao, Henry Chen, Ya-Chuan Hsu +7
A long-standing goal in robotics is to develop generalist robots that can perform tasks across a wide range of settings, flexibly specified with images and text. Vision-Language-Ac…
StereoEngine: Scaling Stereo Bimanual Robot Data without Stereo Cameras
Jason Chen, I-Chun Arthur Liu, Gaurav Sukhatme +1
Stereo vision is increasingly used for robotic manipulation, with a growing body of work incorporating it into Vision-Language-Action (VLA) models. However, the scarcity of large-s…
ExStereo: Lifting 2D Vision-Language-Action Models to 3D with Explicit Stereo Representations
I-Chun Arthur Liu, Jason Chen, Gaurav S. Sukhatme +1
Three-dimensional perception is critical for robotic manipulation, particularly for high-precision tasks, as recovering metric depth and precise 3D object positions from monocular…
HOTICE: Whole-Body Humanoid Object Transportation in Cluttered Environments
Toan Nguyen, Weiduo Yuan, Siheng Zhao +2
Object transportation is a fundamental capability for humanoid robots operating in real-world, human-centric environments, yet existing methods struggle when clutter constrains fre…
Red-Teaming Vision-Language-Action Models via Quality Diversity Prompt Generation for Robust Robot Policies
Siddharth Srikanth, Freddie Liang, Ya-Chuan Hsu +9
Vision-Language-Action (VLA) models have significant potential to enable general-purpose robotic systems for a range of vision-language tasks. However, the performance of VLA-based…
Preference-Conditioned Reinforcement Learning for Space-Time Efficient Online 3D Bin Packing
Nikita Sarawgi, Omey M. Manyar, Fan Wang +3
Robotic bin packing is widely deployed in warehouse automation, with current systems achieving robust performance through heuristic and learning-based strategies. These systems mus…
Causally Robust Reward Learning from Reason-Augmented Preference Feedback
Minjune Hwang, Yigit Korkmaz, Daniel Seita +1
Preference-based reward learning is widely used for shaping agent behavior to match a user's preference, yet its sparse binary feedback makes it especially vulnerable to causal con…
ICLR: In-Context Imitation Learning with Visual Reasoning
Toan Nguyen, Weiduo Yuan, Songlin Wei +3
In-context imitation learning enables robots to adapt to new tasks from a small number of demonstrations without additional training. However, existing approaches typically conditi…
D-REX: Differentiable Real-to-Sim-to-Real Engine for Learning Dexterous Grasping
Haozhe Lou, Mingtong Zhang, Haoran Geng +9
Simulation provides a cost-effective and flexible platform for data generation and policy learning to develop robotic systems. However, bridging the gap between simulation and real…
ROPA: Synthetic Robot Pose Generation for RGB-D Bimanual Data Augmentation
Jason Chen, I-Chun Arthur Liu, Gaurav Sukhatme +1
Training robust bimanual manipulation policies via imitation learning requires demonstration data with broad coverage over robot poses, contacts, and scene contexts. However, colle…
D-CODA: Diffusion for Coordinated Dual-Arm Data Augmentation
I-Chun Arthur Liu, Jason Chen, Gaurav Sukhatme +1
Learning bimanual manipulation is challenging due to its high dimensionality and tight coordination required between two arms. Eye-in-hand imitation learning, which uses wrist-moun…
VoxAct-B: Voxel-Based Acting and Stabilizing Policy for Bimanual Manipulation
I-Chun Arthur Liu, Sicheng He, Daniel Seita +1
Bimanual manipulation is critical to many robotics applications. In contrast to single-arm manipulation, bimanual manipulation tasks are challenging due to higher-dimensional actio…