1 citations · 1 across the 5 of their papers we have counts for
6 papers
Imagining Goals with Geometric Horizon Models for Policy Generalization
Daniel Lawson, Dhruv Sreenivas, Adriana Hugessen +2
Training from offline data has allowed for substantial progress in domains such as robotics, leading to general-purpose policies that can be easily applied zero-shot or efficiently…
Self-Predictive Representations for Combinatorial Generalization in Behavioral Cloning
Daniel Lawson, Adriana Hugessen, Charlotte Cloutier +2
While goal-conditioned behavior cloning (GCBC) methods can perform well on in-distribution training tasks, they do not necessarily generalize zero-shot to tasks that require condit…
Differentiable Composite Neural Signed Distance Fields for Robot Navigation in Dynamic Indoor Environments
S. Talha Bukhari, Daniel Lawson, Ahmed H. Qureshi
Neural Signed Distance Fields (SDFs) provide a differentiable environment representation to readily obtain collision checks and well-defined gradients for robot navigation tasks. H…
Merging Decision Transformers: Weight Averaging for Forming Multi-Task Policies
Daniel Lawson, Ahmed H. Qureshi
Recent work has shown the promise of creating generalist, transformer-based, models for language, vision, and sequential decision-making problems. To create such models, we general…
Co-learning Planning and Control Policies Constrained by Differentiable Logic Specifications
Zikang Xiong, Daniel Lawson, Joe Eappen +2
Synthesizing planning and control policies in robotics is a fundamental task, further complicated by factors such as complex logic specifications and high-dimensional robot dynamic…
Control Transformer: Robot Navigation in Unknown Environments through PRM-Guided Return-Conditioned Sequence Modeling
Daniel Lawson, Ahmed H. Qureshi
Learning long-horizon tasks such as navigation has presented difficult challenges for successfully applying reinforcement learning to robotics. From another perspective, under know…