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Kejun Li

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6 papers
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6

IROS Conference 2025 Conference Paper

Hybrid Data-Driven Predictive Control for Robust and Reactive Exoskeleton Locomotion Synthesis

  • Kejun Li
  • Jeeseop Kim
  • Maxime Brunet
  • Marine Pétriaux
  • Yisong Yue
  • Aaron D. Ames

Robust bipedal locomotion in exoskeletons requires the ability to dynamically react to changes in the environment in real time. This paper introduces the hybrid data-driven predictive control (HDDPC) framework, an extension of the data-enabled predictive control, that addresses these challenges by simultaneously planning foot contact schedules and continuous domain trajectories. The proposed framework utilizes a Hankel matrix-based representation to model system dynamics, incorporating step-to-step (S2S) transitions to enhance adaptability in dynamic environments. By integrating contact scheduling with trajectory planning, the framework offers an efficient, unified solution for locomotion motion synthesis that enables robust and reactive walking through online replanning. We validate the approach on the Atalante exoskeleton, demonstrating improved robustness and adaptability.

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TMLR Journal 2025 Journal Article

Preferential Multi-Objective Bayesian Optimization

  • Raul Astudillo
  • Kejun Li
  • Maegan Tucker
  • Chu Xin Cheng
  • Aaron Ames
  • Yisong Yue

Preferential Bayesian optimization (PBO) is a framework for optimizing a decision-maker’s latent preferences over available design choices. While real-world problems often involve multiple conflicting objectives, existing PBO methods assume that preferences can be encoded by a single objective function. For instance, in the customization of robotic assistive devices, technicians aim to maximize user comfort while minimizing energy consumption to extend battery life. Likewise, in autonomous driving policy design, stakeholders must evaluate safety and performance trade-offs before committing to a policy. To bridge this gap, we introduce the first framework for PBO with multiple objectives. Within this framework, we propose dueling scalarized Thompson sampling (DSTS), a multi-objective generalization of the popular dueling Thompson sampling algorithm, which may also be of independent interest beyond our setting. We evaluate DSTS across four synthetic test functions and two simulated tasks—exoskeleton personalization and driving policy design—demonstrating that it outperforms several benchmarks. Finally, we prove that DSTS is asymptotically consistent. Along the way, we provide, to our knowledge, the first convergence guarantee for dueling Thompson sampling in single-objective PBO.

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IROS Conference 2024 Conference Paper

Data-Driven Predictive Control for Robust Exoskeleton Locomotion

  • Kejun Li
  • Jeeseop Kim
  • Xiaobin Xiong
  • Kaveh Akbari Hamed
  • Yisong Yue
  • Aaron D. Ames

Exoskeleton locomotion must be robust while being adaptive to different users with and without payloads. To address these challenges, this work introduces a data-driven predictive control (DDPC) framework to synthesize walking gaits for lower-body exoskeletons, employing Hankel matrices and a state transition matrix for its data-driven model. The proposed approach leverages DDPC through a multi-layer architecture. At the top layer, DDPC serves as a planner employing Hankel matrices and a state transition matrix to generate a data-driven model that can learn and adapt to varying users and payloads. At the lower layer, our method incorporates inverse kinematics and passivity-based control to map the planned trajectory from DDPC into the full-order states of the lower-body exoskeleton. We validate the effectiveness of this approach through numerical simulations and hardware experiments conducted on the Atalante lower-body exoskeleton with different payloads. Moreover, we conducted a comparative analysis against the model predictive control (MPC) framework based on the reduced-order linear inverted pendulum (LIP) model. Through this comparison, the paper demonstrates that DDPC enables robust bipedal walking at various velocities while accounting for model uncertainties and unknown perturbations.

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IROS Conference 2024 Conference Paper

Dynamic Walking on Highly Underactuated Point Foot Humanoids: Closing the Loop between HZD and HLIP

  • Adrian B. Ghansah
  • Jeeseop Kim
  • Kejun Li
  • Aaron D. Ames

Realizing bipedal locomotion on humanoid robots with point feet is especially challenging due to their highly underactuated nature, high degrees of freedom, and hybrid dynamics resulting from impacts. With the goal of addressing this challenging problem, this paper develops a control framework for realizing dynamic locomotion and implements it on a novel point foot humanoid: ADAM. To this end, we close the loop between Hybrid Zero Dynamics (HZD) and Hybrid linear inverted pendulum (HLIP) based step length regulation. To leverage the full-order hybrid dynamics of the robot, walking gaits are first generated offline by utilizing HZD. These trajectories are stabilized online through the use of a HLIP based regulator. Finally, the planned trajectories are mapped into the full-order system using a task space controller incorporating inverse kinematics. The proposed method is verified through numerical simulations and hardware experiments on the humanoid robot ADAM marking the first humanoid point foot walking. Moreover, we experimentally demonstrate the robustness of the realized walking via the ability to track a desired reference speed, robustness to pushes, and locomotion on uneven terrain.

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ICRA Conference 2024 Conference Paper

Synthesizing Robust Walking Gaits via Discrete-Time Barrier Functions with Application to Multi-Contact Exoskeleton Locomotion

  • Maegan Tucker
  • Kejun Li
  • Aaron D. Ames

Successfully achieving bipedal locomotion remains challenging due to real-world factors such as model uncertainty, random disturbances, and imperfect state estimation. In this work, we propose a novel metric for locomotive robustness – the estimated size of the hybrid forward invariant set associated with the step-to-step dynamics. Here, the forward invariant set can be loosely interpreted as the region of attraction for the discrete-time dynamics. We illustrate the use of this metric towards synthesizing nominal walking gaits using a simulation-in-the-loop learning approach. Further, we leverage discrete-time barrier functions and a sampling-based approach to approximate sets that are maximally forward invariant. Lastly, we experimentally demonstrate that this approach results in successful locomotion for both flat-foot walking and multi-contact walking on the Atalante lower-body exoskeleton.

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ICRA Conference 2021 Conference Paper

ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes

  • Kejun Li
  • Maegan Tucker
  • Erdem Biyik
  • Ellen R. Novoseller
  • Joel W. Burdick
  • Yanan Sui
  • Dorsa Sadigh
  • Yisong Yue

Characterizing what types of exoskeleton gaits are comfortable for users, and understanding the science of walking more generally, require recovering a user’s utility landscape. Learning these landscapes is challenging, as walking trajectories are defined by numerous gait parameters, data collection from human trials is expensive, and user safety and comfort must be ensured. This work proposes the Region of Interest Active Learning (ROIAL) framework, which actively learns each user’s underlying utility function over a region of interest that ensures safety and comfort. ROIAL learns from ordinal and preference feedback, which are more reliable feedback mechanisms than absolute numerical scores. The algorithm’s performance is evaluated both in simulation and experimentally for three non-disabled subjects walking inside of a lower-body exoskeleton. ROIAL learns Bayesian posteriors that predict each exoskeleton user’s utility landscape across four exoskeleton gait parameters. The algorithm discovers both commonalities and discrepancies across users’ gait preferences and identifies the gait parameters that most influenced user feedback. These results demonstrate the feasibility of recovering gait utility landscapes from limited human trials.

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