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
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)
Object-centric Self-supervised Reinforcement Learning
Autonomous agents need large repertoires of skills to act reasonably on new tasks that they have not seen before. To learn such skills an agent can first learn a structured representation of the environment and then use it for the construction of the goal space. In particular, we propose to use object-centric representations learned from images without supervision.
In the first project [
], we showed that object-centric representations could be learned from image observations collected by the random agent. Such representations serves as subgoals and could be used to solve challenging rearranging and pushing tasks in the environment without any external goals.
In the second project [], we investigate the problem of composing object-centric subgoals to solve complex compositional tasks, such as rearranging up to 6 different objects. To tackle this problem, we first infer the object interaction graph from object dynamics data and then use it for the construction of the independently controllable subgoals. Such subgoals could be combined in a sequence of compatible subgoals and allow the agent to solve each subgoal without destroying previously solved subgoals.
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