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My research focuses on developing new theoretical foundations for stochastic motion planning to account for diverse and varying uncertainty descriptions while retaining the tractability of the state-of-the-art approaches. The theory has practical and scalable implementation and has been deployed not just in controlled laboratory settings but also in complex, real-world environments.

Conformal Prediction (CP) is a statistical technique for quantifying the uncertainty of data-driven models in a distribution-free manner. We utilize these techniques for ensuring a statistically guaranteed level of task success while,

  1. Adapting to distribution shifts in motion planning with unknown dynamic agents, and

  2. Aligning the uncertainty of LLM-based planners by allowing the robot to ask for help when the language instruction is unclear.

A structured method for a moving agent to predict the paths of dynamically moving obstacles and avoid them using a risk-aware planning scheme. 

Synthesis and evaluation of increasingly dynamic quadrupedal locomotion gaits like walking, trotting, and hopping for navigation over unknown terrain with a focus on obstacle avoidance using the walking gait and vision.

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