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Contact

Contact Lening Li by email.

Curriculum Vitae

Curriculum vitae and one-page resume of Lening Li (English and Chinese), compiled from the latest LaTeX source.

Education

Education of Lening Li: MBA at Carnegie Mellon (Tepper), Ph.D. and M.S. degrees from WPI, B.E. and B.A. from Harbin Institute of Technology.

Experience

Entrepreneurship, research, and industry experience of Lening Li: co-founder of a startup in stealth, Yale University, Harvard University, Symbotic, and Berkshire Grey.

Lening Li (郦乐宁)

Co-founder building physical AI in stealth, Ph.D. in Robotics Engineering, and MBA candidate at Carnegie Mellon Tepper.

Misc

A searchable list of classic computer science and robotics books, and side projects by Lening Li: LineupIQ, MulVAL to PDDL, and PDDL Parser.

Publications

Publications by Lening Li — conference and journal papers on temporal-logic planning, reinforcement learning, game-theoretic security, and humanoid robotics, with PDFs and BibTeX.

Research

Research by Lening Li: Kinex and agentic AI for robotics, reinforcement learning and optimal control under temporal-logic specifications, hypergames for cyber-physical security, anytime motion planning, and the DARPA Robotics Challenge.

Service

Academic service, leadership and honors of Lening Li: journal and conference reviewing, WPI Graduate Student Government presidency, memberships and awards.

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Teaching

Teaching and mentoring by Lening Li: WPI robotics and computer-vision courses, Harvard research advising, and VEX / FIRST robotics coaching.

Travel

Places Lening Li has travelled for conferences and collaborations.

Anytime Planning via Function Approximation and Importance Sampling

Motion planning for nonlinear robotic systems is computationally intractable in general (PSPACE-hard), so approximate schemes — discretization-based (A*) and sampling-based (RRT*) — dominate practice. We explore a third approach: function approximation, which transforms planning in the state space into planning in a low-dimensional parameter space of policy function approximators.

DARPA Robotics Challenge

Formal Methods and Game Theory for Cyber-Physical Security

In an adversarial environment, information — and the lack of it — is decisive for strategic decision-making. We developed a class of hypergames on graphs to model the interaction between an intelligent robot and its adversary when the task is given in temporal logic and the two players hold asymmetric information.

Kinex: One Agent for Any Robot

A robotics harness that connects coding agents to real hardware and simulators through skills, tools, and devices, including ROS 2 integration.

Learning-based Planning with Temporal Logic Constraints

The goal is a model-free reinforcement learning method for stochastic planning under temporal logic constraints — so that a robot can be given a high-level task and return a policy that provably satisfies it.