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About

Sangli Teng is a robotics researcher whose work lies at the intersection of geometric perception, state estimation, and control for legged and mobile robots operating in challenging, real-world environments. His most impactful contribution is the development of a state estimator for legged robots in slippery conditions, which uses an Invariant Extended Kalman Filter (InEKF) to fuse inertial data with velocity updates from a tracking camera and leg kinematics—a paper that has garnered 54 citations. Teng has also pioneered an error-state Model Predictive Control (MPC) framework on connected matrix Lie groups, enabling more stable and precise robot control. His research consistently leverages geometric symmetry and Lie group theory to create robust, symmetry-preserving algorithms for perception and control, as summarized in a widely-read progress article (13 citations). Notably, he has advanced safety-aware informative motion planning by integrating Control Barrier Functions with information-gathering planners, and developed a fully proprioceptive slip-velocity-aware estimator using invariant Kalman filtering and disturbance observers (17 citations). His recent work extends state estimation to non-inertial environments and introduces convex geometric motion planning for multi-body systems. With over 140 total citations across his key publications, Teng is establishing himself as a leading voice in geometrically principled, safety-critical robot autonomy.