CV
/
/

Projects

Project Pages

Overview diagram of the Online Adaptive ICCBF algorithm applied to MPC framework.
The Online Adaptive ICCBF algorithm dynamically adapts Input Constrained Control Barrier Function (ICCBF) parameters to optimize performance while ensuring safety for input-constrained nonlinear systems. Our approach leverages a Probabilistic Ensemble Neural Network (PENN) to predict performance and risk metrics, considering both epistemic and aleatoric uncertainties. The algorithm incorporates a two-step verification process using Jensen-Rényi Divergence (JRD) and Distributionally-Robust Conditional Value at Risk (DR-CVaR) to identify valid parameters. By adapting ICCBF parameters online based on the current state and nearby environment, our method optimizes performance while maintaining safety.

Motivation

Control Barrier Functions (CBFs) are widely used in robotics to ensure system safety. However, finding valid CBFs that guarantee persistent safety and feasibility remains an open challenge, especially in systems with input constraints. Traditional approaches often rely on manually tuning the parameters of the class K functions for the CBF conditions a priori. The performance of CBF-based controllers is highly sensitive to these fixed parameters, potentially leading to overly conservative behavior (such as deadlock) or safety violations (due to infeasibility).

Algorithm Breakdown

Data Generation

We initially generate robot trajectories using the CBF-based controller to form the training dataset by varying the robot's initial state, obstacle configurations, and class-K function parameters. The risk level and deadlock time are recorded as the ground truth for prediction. The risk level is computed as the maximum safety loss value during navigation, defined by a safety loss density function that captures the collision risk.
[ICRA 2025] Online Adaptive ICCBF
Main illustration of the Visibility-Aware RRT* algorithm.
The LQR-CBF-Steer function is used as a steering method in RRT* to generate safe trajectories between two nodes in the tree. Our Visibility-Aware RRT* algorithm incorporates two control barrier function (CBF) constraints into the LQR-CBF-Steer function to generate safe and efficient paths for robots with limited sensing capabilities. The collision avoidance CBF ensures the planned path remains collision-free w.r.t. known obstacles, while the novel visibility CBF guarantees the robot stays within locally collision-free regions, enabling timely detection and avoidance of unknown obstacles. These CBF constraints serve as termination criteria during the steering process, ensuring that the generated paths are both collision-free and visibility-aware.

Motivation

Collision with a hidden obstacle due to limited sensing capabilities and a perception-agnostic global planner.
Safe autonomous navigation in unknown environments is challenging for robots with limited sensing capabilities. The effectiveness of safety-critical control techniques, such as Control Barrier Functions (CBFs), relies on the assumption of complete environmental knowledge. The video demonstrates that a CBF-based safety-critical controller (CBF-QP) becomes infeasible and collides with a hidden obstacle while tracking a reference path generated by the standard LQR-RRT*, which is agnostic to the robot's sensing limitations. This illustrates that if the global planner does not account for the robot's limited perception, the theoretical safety guarantees may not hold for the entire navigation stack, leading to potentially hazardous situations. This highlights the need for a planning algorithm that explicitly considers the robot's limited sensing capabilities to ensure safe navigation in partially unknown environments.

Preview Experiments

Visualization of the global planning results generated by the Visibility-Aware RRT*, without and with incorporating visibility CBF. The blue and yellow squares represent the start and goal positions. The black circles represent the known obstacles. The red line depict the final reference path. The shaded areas in gray represent the local collision-free set that the robot will sense while following the reference path.
Visibility-Aware RRT*
Overview diagram of our unified model-based reinforcement learning framework with dynamics learning. In exploration phase, a parallelized ensemble neural network serves as the robot dynamics and outputs the estimated posterior distribution of the next state. To enable active exploration, we quantify epistemic uncertainty by measuring the ensemble disagreement via Jensen-Rényi Divergence. In deployment phase, the neural network dynamics trained during the active exploration phase is applied directly to perform uncertainty-aware control. We transfer the neural network dynamics for uncertainty-aware deployment with minimal modification.

Video

Main Supplmentary Video

Real-World Demo

We successfully transferred our algorithm to the real-world settings for uncertainty-aware deployment tasks. These are our initial real-world experiments using zero-shot transfer, without considering sim-to-real techniques such as domain adaptation or domain randomization. We integrated our algorithm with global path planning and online traversability map generation using a LiDAR sensor. These experiments were conducted on our off-road testbeds. See ‘Future Work’ section below for more details.
[RSS 2023] Bridging Active Exploration and Uncertainty-Aware Deployment
[RAL 2023] Learning Terrain-Aware Kinodynamic Model for Off-Road Driving
A simplified representation of the MPPI algorithm during each optimization iteration. For clarity, we only visualize one sampled trajectory (in green). (a) Amount of changes between previously computed control sequence and the next control sequence (along the “i-axis”). (b) Amount of changes in control values during MPPI rollouts (along the “t-axis”), which are hard to be minimized by the MPPI baseline. Such chattering in control input becomes more prominent in cases where the environment changes rapidly, possibly even causing the MPPI to diverge.
To address this issue, we propose the Smooth MPPI algorithm that seamlessly combines MPPI with an input-lifting strategy.
Main Supplmentary Video
[RAL 2022] Smooth MPPI

Other Projects

Traversability Estimation
Deep Learning
Traversability
Off-Road Autonomous Driving
Dynamic Learning
Uncertainty-Aware Active Exploration
MBRL
Dynamic Learning
Learning-Based Vehicle Model and Control
Dynamic Learning
Control
Smooth MPPI
Control
DPoom
Robot System
Navigation