22 citations · 35 across the 6 of their papers we have counts for
8 papers · 1 filter
See, Hear, and Feel: Smart Sensory Fusion for Robotic Manipulation
Hao Li, Yizhi Zhang, Junzhe Zhu +7
Humans use all of their senses to accomplish different tasks in everyday activities. In contrast, existing work on robotic manipulation mostly relies on one, or occasionally two mo…
Differentiable Factor Graph Optimization for Learning Smoothers
Brent Yi, Michelle A. Lee, Alina Kloss +2
A recent line of work has shown that end-to-end optimization of Bayesian filters can be used to learn state estimators for systems whose underlying models are difficult to hand-des…
Interpreting Contact Interactions to Overcome Failure in Robot Assembly Tasks
Peter A. Zachares, Michelle A. Lee, Wenzhao Lian +1
A key challenge towards the goal of multi-part assembly tasks is finding robust sensorimotor control methods in the presence of uncertainty. In contrast to previous works that rely…
Detect, Reject, Correct: Crossmodal Compensation of Corrupted Sensors
Michelle A. Lee, Matthew Tan, Yuke Zhu +1
Using sensor data from multiple modalities presents an opportunity to encode redundant and complementary features that can be useful when one modality is corrupted or noisy. Humans…
Multimodal Sensor Fusion with Differentiable Filters
Michelle A. Lee, Brent Yi, Roberto Martín-Martín +2
Leveraging multimodal information with recursive Bayesian filters improves performance and robustness of state estimation, as recursive filters can combine different modalities acc…
Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks
Roberto Martín-Martín, Michelle A. Lee, Rachel Gardner +3
Reinforcement Learning (RL) of contact-rich manipulation tasks has yielded impressive results in recent years. While many studies in RL focus on varying the observation space or re…
Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks
Michelle A. Lee, Yuke Zhu, Peter Zachares +6
Contact-rich manipulation tasks in unstructured environments often require both haptic and visual feedback. It is non-trivial to manually design a robot controller that combines th…
Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks
Michelle A. Lee, Yuke Zhu, Krishnan Srinivasan +5
Contact-rich manipulation tasks in unstructured environments often require both haptic and visual feedback. However, it is non-trivial to manually design a robot controller that co…