MSR Thesis Presentation

Abstract: The physical properties and dynamics that decide how an object or environment responds to a robot's actions are often impossible to determine from visual observation alone. An object's mass distribution and friction, the kinematics of an articulated object: these latent factors dictate the correct action, yet they leave little or no trace in a single [...]

Abstract: Visual imitation learning is a promising approach to training robot manipulation policies capable of completing a wide variety of tasks. A key requirement for these manipulation policies is to exhibit robust generalization capabilities when deployed in the real world, where the objects, scenes, and sensors a robot encounters differ from those seen during training. In [...]

Abstract: Hierarchical imitation learning has become an effective approach for robotic manipulation: a high-level policy predicts a sub-goal end-effector pose, while a low-level policy executes the actions needed to reach it. This decomposition improves generalization and provides an interpretable interface, but the design of the high-level goal predictor remains an open question. This thesis studies [...]

Abstract: Generalist robot policies, vision-language-action models that combine a large pretrained vision-language model backbone with a diffusion or flow-matching action head, are increasingly capable, yet hard to deploy in the real world. Three gaps separate such a policy from a deployable one: a data gap (adapting to a new task still demands task-specific teleoperation data), an inference gap (the policy [...]