Fairness meets CMDPs
This paper proposes SCALES, a general framework that translates well-established fairness principles into a common representation based on CMDPs.
Robot & Machine Learning
Collaborative Learning and Adaptive Robots (CLeAR) Lab.
Below, you’ll find our contributions to robot/machine learning. Much of our work is on learning from (or interacting with) humans. However, you'll also find some works that are purely curiosity-driven. For example, at CLeAR, we have developed new deep generative models that are based on gradient flows and structured temporal models that can capture complex relationships, yet are interpretable and useful for planning and decision-making. We believe this works are crucial for finding "out-of-the-box" ideas and novel techniques for developing robot skills.
The Dynamics of Q-learning in Population Games
We develop an accurate physics-inspired model for describing how a population of Q-learning agents adapt as they interact.
MIRROR: Differentiable Deep Social Projection for Assistive Human-Robot Communication
Inspired by Social Projection Theory, we use the robot's self model to efficiently model humans.
Deep Explicit Duration Switching Models for Time Series
We propose a deep switching state space model that can capture both state-dependent and time-dependent switching patterns in time series data.
Embedding Symbolic Temporal Knowledge into Deep Sequential Models
We embed symbolic knowledge expressed as linear temporal logic (LTL) and use these embeddings to guide the training of deep sequential models.
Learning Robust Latent Representation for Reinforcement Learning with Multi-Modal Observations
We construct a shared latent space from different sensory modalities via contrastive learning.
Enhancing Deep Learning with Symbolic Knowledge
Bridging the gap between symbolic and connectionist paradigms via Graph Neural Network embeddings
Juiced and Ready to Predict Private Information in Deep Cooperative Reinforcement Learning
Training robots that can interactively assist humans with private information
Embedding Symbolic Knowledge into Deep Networks
Leveraging prior symbolic knowledge to improve the performance of deep models.
Efficiently Exploring Reward Functions in Inverse RL
Using Bayesian Optimization to address the ill-posed nature of Inverse Reinforcement Learning