Embodied AI — reinforcement learning and active perception.

• Designed a two-phase MDP framework that separates goal inference from goal-conditioned planning; recovers the optimal strategy when observations are informative.
• Built a feature-based heuristic model (linear regression) that captures most human behavior and isolates the contribution of goal inference.
• Introduced a mixed-strategy model (logistic regression over weighted combinations) that explains residual variance beyond the optimal strategy.
• Vectorized single-goal heuristic features to extend to a multi-goal setting without retraining.

• {"5× faster teleoperation" => "motion-rate factor 0.01 → 0.05 while keeping trajectories smooth."}
• ~90% fewer interruptions during long sessions via buffering, deadband tuning, reconnection protocol.
• End-to-end gripper open/close control in the recorded trajectory stream.

• Designed and implemented the Franka Research 3 driver in the hardware abstraction layer (HAL).
• Lets LLM agents take over Franka Research 3 without per-user environment setup.

• End-to-end pipeline for world-model data on Franka Research 3.

• Active perception research (Matlab).