Human Pose, Shape and Action
3D Pose from Images
2D Pose from Images
Beyond Motion Capture
Action and Behavior
Body Perception
Body Applications
Pose and Motion Priors
Clothing Models (2011-2015)
Reflectance Filtering
Learning on Manifolds
Markerless Animal Motion Capture
Multi-Camera Capture
2D Pose from Optical Flow
Body Perception
Neural Prosthetics and Decoding
Part-based Body Models
Intrinsic Depth
Lie Bodies
Layers, Time and Segmentation
Understanding Action Recognition (JHMDB)
Intrinsic Video
Intrinsic Images
Action Recognition with Tracking
Neural Control of Grasping
Flowing Puppets
Faces
Deformable Structures
Model-based Anthropometry
Modeling 3D Human Breathing
Optical flow in the LGN
FlowCap
Smooth Loops from Unconstrained Video
PCA Flow
Efficient and Scalable Inference
Motion Blur in Layers
Facade Segmentation
Smooth Metric Learning
Robust PCA
3D Recognition
Object Detection
Causal Reasoning in RL
Reinforcement learning is fundamentally a causal endeavor. The agent intervenes in the environment through actions and observes their effects; learning through credit assignment involves the counterfactual question whether another action would have resulted in a better outcome. Causal Reasoning for RL is about learning and using causal knowledge to improve RL algorithms. We are interested in using tools from causality to make RL algorithms more robust and sample efficient.
In a first project [, we showed how learning a causal model of agent-object interactions allows to infer whether the agent has causal influence on its environment. We then demonstrated how this information can be integrated into RL algorithms to steer the exploration process and improve sample-efficiency of off-policy training. Experiments on robotic manipulation benchmarks show a clear improvement over state-of-the-art.
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