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Research Overview

Intrinsically Motivated Learning

Regularity as Intrinsic Reward for Free Play

SENSEI: Semantic Exploration Guided by Foundation Models to Learn Versatile World Models

Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation

Learning with Muscles

Natural and Robust Walking from Generic Rewards

The effect of muscles in Learning Behavior

Scaling RL to Large Musculoskeletal Systems

Reinforcement Learning for Diverse Solutions

Offline Diversity Under Imitation Constraints

Learning Diverse Skills for Local Navigation

Learning Agile Skills via Adversarial Imitation of Rough Partial Demonstrations

Reinforcement Learning and Control

Model-based Reinforcement Learning and Planning

Object-centric Self-supervised Reinforcement Learning

Self-exploration of Behavior

Causal Reasoning in RL

Equation Learner for Extrapolation and Control

Intrinsically Motivated Hierarchical Learner

Regularity as Intrinsic Reward for Free Play

Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation

Natural and Robust Walking from Generic Rewards

Goal-conditioned Offline Planning

Offline Diversity Under Imitation Constraints

Learning Diverse Skills for Local Navigation

Learning Agile Skills via Adversarial Imitation of Rough Partial Demonstrations

Deep Learning

Combinatorial Optimization as a Layer / Blackbox Differentiation

Object-centric Self-supervised Reinforcement Learning

Symbolic Regression and Equation Learning

Representation Learning

Stepsize adaptation for stochastic optimization

Probabilistic Neural Networks

Learning with 3D rotations: A hitchhiker’s guide to SO(3)

ML for Science

Predicting brain activity (fMRI)

Equation Learning for Statistical Physics

Machine Learning for Understanding Quantum Systems

Symbolic Regression and Equation Learning

Previous Research Projects

The Playful Machine

Robust and Affordable Haptic Sensation with Sparse Sensor Configuration

Autonomous Learning

Haptic Sensing

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Robots like Poppy need haptic feedback for small-scale (green highlighted fingertip — middle left) and large-scale (white limb — bottom left) surfaces. We aim for creating robust haptic sensors with super-resolution property that reduces taxel number, wiring, and manufacturing costs. Machine learning methods are employed to interpret non-linear raw sensor values into real-world values and timely deliver the host robot needed haptic information, such as contact locations and forces.

The rapid evolution of robotic technologies informs practical benefits in various physical application areas. In complex, changing, and especially human-involved scenarios, a robot must be well-equipped to perceive the interactions between its own body and other things. Due to the visual occlusion and the small scale of the deformations during interactions, robots need touch-sensitive skin in addition to well-developed vision feedback. In this research field, we explore creating durable and robust haptic sensors to meet various application requirements and design machine-learning algorithms to enhance the data processing flow.