31 citations · 178 across the 53 of their papers we have counts for
5 papers · 1 filter
Learning to Follow Language Instructions with Compositional Policies
Vanya Cohen, Geraud Nangue Tasse, Nakul Gopalan +3
We propose a framework that learns to execute natural language instructions in an environment consisting of goal-reaching tasks that share components of their task descriptions. Ou…
Towards Sample-efficient Apprenticeship Learning from Suboptimal Demonstration
Letian Chen, Rohan Paleja, Matthew Gombolay
Learning from Demonstration (LfD) seeks to democratize robotics by enabling non-roboticist end-users to teach robots to perform novel tasks by providing demonstrations. However, as…
Improving Robot-Centric Learning from Demonstration via Personalized Embeddings
Mariah L. Schrum, Erin Hedlund, Matthew C. Gombolay
Learning from demonstration (LfD) techniques seek to enable novice users to teach robots novel tasks in the real world. However, prior work has shown that robot-centric LfD approac…
Heterogeneous Graph Attention Networks for Learning Diverse Communication
Esmaeil Seraj, Zheyuan Wang, Rohan Paleja +3
Multi-agent teaming achieves better performance when there is communication among participating agents allowing them to coordinate their actions for maximizing shared utility. Howe…
Natural Language Specification of Reinforcement Learning Policies through Differentiable Decision Trees
Pradyumna Tambwekar, Andrew Silva, Nakul Gopalan +1
Human-AI policy specification is a novel procedure we define in which humans can collaboratively warm-start a robot's reinforcement learning policy. This procedure is comprised of…