9 citations · 12 across the 6 of their papers we have counts for
6 papers
Demonstrating Multi-Suction Item Picking at Scale via Multi-Modal Learning of Pick Success
Che Wang, Jeroen van Baar, Chaitanya Mitash +6
This work demonstrates how autonomously learning aspects of robotic operation from sparsely-labeled, real-world data of deployed, engineered solutions at industrial scale can provi…
Learning to Synthesize Volumetric Meshes from Vision-based Tactile Imprints
Xinghao Zhu, Siddarth Jain, Masayoshi Tomizuka +1
Vision-based tactile sensors typically utilize a deformable elastomer and a camera mounted above to provide high-resolution image observations of contacts. Obtaining accurate volum…
Cross-domain Imitation from Observations
Dripta S. Raychaudhuri, Sujoy Paul, Jeroen van Baar +1
Imitation learning seeks to circumvent the difficulty in designing proper reward functions for training agents by utilizing expert behavior. With environments modeled as Markov Dec…
Data-Efficient Learning for Complex and Real-Time Physical Problem Solving using Augmented Simulation
Kei Ota, Devesh K. Jha, Diego Romeres +7
Humans quickly solve tasks in novel systems with complex dynamics, without requiring much interaction. While deep reinforcement learning algorithms have achieved tremendous success…
Learning from Trajectories via Subgoal Discovery
Sujoy Paul, Jeroen van Baar, Amit K. Roy-Chowdhury
Learning to solve complex goal-oriented tasks with sparse terminal-only rewards often requires an enormous number of samples. In such cases, using a set of expert trajectories coul…
Trajectory-based Learning for Ball-in-Maze Games
Sujoy Paul, Jeroen van Baar
Deep Reinforcement Learning has shown tremendous success in solving several games and tasks in robotics. However, unlike humans, it generally requires a lot of training instances.…