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Chao Qin

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8 papers
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8

ICRA Conference 2024 Conference Paper

Time-Optimal Gate-Traversing Planner for Autonomous Drone Racing

  • Chao Qin
  • Maxime S. J. Michet
  • Jingxiang Chen
  • Hugh H. T. Liu

In drone racing, the time-minimum trajectory is affected by the drone’s capabilities, the layout of the race track, and the configurations of the gates (e. g. , their shapes and sizes). However, previous studies neglect the configuration of the gates, simply rendering drone racing a waypoint-passing task. This formulation often leads to a conservative choice of paths through the gates, as the spatial potential of the gates is not fully utilized. To address this issue, we present a time-optimal planner that can faithfully model gate constraints with various configurations and thereby generate a more time-efficient trajectory while considering the single-rotor-thrust limits. Our approach excels in computational efficiency which only takes a few seconds to compute the full state and control trajectories of the drone through tracks with dozens of different gates. Extensive simulations and experiments confirm the effectiveness of the proposed methodology, showing that the lap time can be further reduced by taking into account the gate’s configuration. We validate our planner in real-world flights and demonstrate super-extreme flight trajectory through race tracks.

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NeurIPS Conference 2022 Conference Paper

An Analysis of Ensemble Sampling

  • Chao Qin
  • Zheng Wen
  • Xiuyuan Lu
  • Benjamin Van Roy

Ensemble sampling serves as a practical approximation to Thompson sampling when maintaining an exact posterior distribution over model parameters is computationally intractable. In this paper, we establish a regret bound that ensures desirable behavior when ensemble sampling is applied to the linear bandit problem. This represents the first rigorous regret analysis of ensemble sampling and is made possible by leveraging information-theoretic concepts and novel analytic techniques that may prove useful beyond the scope of this paper.

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ICML Conference 2022 Conference Paper

Contextual Information-Directed Sampling

  • Botao Hao
  • Tor Lattimore
  • Chao Qin

Information-directed sampling (IDS) has recently demonstrated its potential as a data-efficient reinforcement learning algorithm. However, it is still unclear what is the right form of information ratio to optimize when contextual information is available. We investigate the IDS design through two contextual bandit problems: contextual bandits with graph feedback and sparse linear contextual bandits. We provably demonstrate the advantage of contextual IDS over conditional IDS and emphasize the importance of considering the context distribution. The main message is that an intelligent agent should invest more on the actions that are beneficial for the future unseen contexts while the conditional IDS can be myopic. We further propose a computationally-efficient version of contextual IDS based on Actor-Critic and evaluate it empirically on a neural network contextual bandit.

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ICRA Conference 2020 Conference Paper

LINS: A Lidar-Inertial State Estimator for Robust and Efficient Navigation

  • Chao Qin
  • Haoyang Ye
  • Christian E. Pranata
  • Jun Han
  • Shuyang Zhang
  • Ming Liu 0001

We present LINS, a lightweight lidar-inertial state estimator, for real-time ego-motion estimation. The proposed method enables robust and efficient navigation for ground vehicles in challenging environments, such as feature-less scenes, via fusing a 6-axis IMU and a 3D lidar in a tightly-coupled scheme. An iterated error-state Kalman filter (ESKF) is designed to correct the estimated state recursively by generating new feature correspondences in each iteration, and to keep the system computationally tractable. Moreover, we use a robocentric formulation that represents the state in a moving local frame in order to prevent filter divergence in a long run. To validate robustness and generalizability, extensive experiments are performed in various scenarios. Experimental results indicate that LINS offers comparable performance with the state-of-the-art lidar-inertial odometry in terms of stability and accuracy and has order-of-magnitude improvement in speed.

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IROS Conference 2020 Conference Paper

Robust Pedestrian Tracking in Crowd Scenarios Using an Adaptive GMM-based Framework

  • Shuyang Zhang
  • Di Wang 0028
  • Fulong Ma
  • Chao Qin
  • Zhengyong Chen
  • Ming Liu 0001

In this paper, we address the issue of pedestrian tracking in crowd scenarios. People in close social relationships tend to act as a group which is a great challenge to individually discriminate and track pedestrians on a LiDAR system. In this paper, we integrally model groups of people and track them in a recursive framework based on Gaussian Mixture Model (GMM). The model is optimized by an extended Expectation-Maximization (EM) algorithm which can adaptively vary the number of mixture components over scans. Experimental results both qualitatively and quantitatively indicate the reliability and accuracy of our tracker in populated scenarios.

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NeurIPS Conference 2017 Conference Paper

Improving the Expected Improvement Algorithm

  • Chao Qin
  • Diego Klabjan
  • Daniel Russo

The expected improvement (EI) algorithm is a popular strategy for information collection in optimization under uncertainty. The algorithm is widely known to be too greedy, but nevertheless enjoys wide use due to its simplicity and ability to handle uncertainty and noise in a coherent decision theoretic framework. To provide rigorous insight into EI, we study its properties in a simple setting of Bayesian optimization where the domain consists of a finite grid of points. This is the so-called best-arm identification problem, where the goal is to allocate measurement effort wisely to confidently identify the best arm using a small number of measurements. In this framework, one can show formally that EI is far from optimal. To overcome this shortcoming, we introduce a simple modification of the expected improvement algorithm. Surprisingly, this simple change results in an algorithm that is asymptotically optimal for Gaussian best-arm identification problems, and provably outperforms standard EI by an order of magnitude.

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RLDM Conference 2017 Conference Abstract

Improving the Expected Improvement Algorithm*

  • Chao Qin
  • Diego Klabjan
  • Daniel Russo

The expected improvement (EI) algorithm is a popular strategy for information collection in optimization under uncertainty. The algorithm is widely known to be too greedy, but nevertheless enjoys wide use due to its simplicity and ability to handle uncertainty and noise in a coherent decision theoretic framework. To provide rigorous insight into EI, we study its properties in a simple setting of Bayesian optimization where the domain consists of a finite grid of points. This is the so-called best-arm identification problem, where the goal is to allocate measurement effort wisely to confidently identify the best arm using a small number of measurements. In this framework, one can show formally that EI is far from optimal. To overcome this shortcoming, we introduce a simple modification of the expected improvement algorithm. Surprisingly, this simple change results in an algorithm that is asymptotically optimal for Gaussian best-arm identification problems, and provably outperforms standard EI by an order of magnitude.

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ICRA Conference 2008 Conference Paper

Trajectory inverse kinematics by conditional density modes

  • Chao Qin
  • Miguel Á. Carreira-Perpiñán

We present a machine learning approach for trajectory inverse kinematics: given a trajectory in workspace, to find a feasible trajectory in angle space. The method learns offline a conditional density model of the joint angles given the workspace coordinates. This density implicitly defines the multivalued inverse kinematics mapping for any workspace point. At run time, given a trajectory in the workspace, the method (1) computes the modes of the conditional density given each of the workspace points, and (2) finds the reconstructed angle trajectory by minimising over the set of modes a global, trajectory-wide constraint that penalises discontinuous jumps in angle space or invalid inverses. We demonstrate the method with a PUMA 560 robot arm and show how it can reconstruct the true angle trajectory even when the workspace trajectory contains singularities, and when the number of inverse branches depends on the workspace location.

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