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Found 6 papers, 3 papers with code
MultiBench: Multiscale Benchmarks for Multimodal Representation Learning
In order to accelerate progress towards understudied modalities and tasks while ensuring real-world robustness, we release MultiBench, a systematic and unified large-scale benchmark spanning 15 datasets, 10 modalities, 20 prediction tasks, and 6 research areas.
Detect, Reject, Correct: Crossmodal Compensation of Corrupted Sensors
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.
Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning
Traditional robotic approaches rely on an accurate model of the environment, a detailed description of how to perform the task, and a robust perception system to keep track of the current state.
Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks
Contact-rich manipulation tasks in unstructured environments often require both haptic and visual feedback.
Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks
This paper studies the effect of different action spaces in deep RL and advocates for Variable Impedance Control in End-effector Space (VICES) as an advantageous action space for constrained and contact-rich tasks.
Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks
Contact-rich manipulation tasks in unstructured environments often require both haptic and visual feedback.
