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Fei Miao

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20 papers

2 author rows

TMLR Journal 2026 Journal Article

α-OCC: Uncertainty-Aware Camera-based 3D Semantic Occupancy Prediction

Sanbao Su
Nuo Chen
Chenchen Lin
Felix Juefei-Xu
Chen Feng
Fei Miao

Comprehending 3D scenes is paramount for tasks such as planning and mapping for autonomous vehicles and robotics. Camera-based 3D Semantic Occupancy Prediction (OCC) aims to infer scene geometry and semantics from limited observations. While it has gained popularity due to affordability and rich visual cues, existing methods often neglect the inherent uncertainty in models. To address this, we propose an uncertainty-aware OCC method (α-OCC). We first introduce Depth-UP, an uncertainty propagation framework that improves geometry completion by up to 11.58% and semantic segmentation by up to 12.95% across various OCC models. For uncertainty quantification (UQ), we propose the hierarchical conformal prediction (HCP) method, effectively handling the high-level class imbalance in OCC datasets. On the geometry level, the novel KL-based score function significantly improves the occupied recall (45%) of safety-critical classes with minimal performance overhead (3.4% reduction). On UQ, our HCP achieves smaller prediction set sizes while maintaining the defined coverage guarantee. Compared with baselines, it reduces up to 90% set size, with 18% further reduction when integrated with Depth-UP. Our contributions advance OCC accuracy and robustness, marking a noteworthy step forward in autonomous perception systems. Our code is public on https://coperception.github.io/alpha-OCC/.

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AAAI Conference 2025 Conference Paper

CUQDS: Conformal Uncertainty Quantification Under Distribution Shift for Trajectory Prediction

Huiqun Huang
Sihong He
Fei Miao

Trajectory prediction models that can infer both future trajectories and their associated uncertainties of the target vehicles is crucial for safe and robust navigation and path planning of autonomous vehicles. However, the majority of existing trajectory prediction models have neither considered reducing the uncertainty as one objective during the training stage nor provided reliable uncertainty quantification during inference stage, especially under potential distribution shift. Therefore, in this paper, we propose the Conformal Uncertainty Quantification under Distribution Shift framework, CUQDS, to quantify the uncertainty of the predicted trajectories of existing trajectory prediction models under potential data distribution shift, while improving the prediction accuracy of the models and reducing the estimated uncertainty during the training stage. Specifically, CUQDS includes 1) a learning-based Gaussian process regression module that models the output distribution of the base model (any existing trajectory prediction neural networks) and reduces the estimated uncertainty by an additional loss term, and 2) a statistical-based Conformal P control module to calibrate the estimated uncertainty from the Gaussian process regression module in an online setting under potential distribution shift between training and testing data. Experimental results on various state-of-the-art methods using benchmark motion forecasting datasets demonstrate the effectiveness of our proposed design.

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IROS Conference 2025 Conference Paper

Multi-Agent Reinforcement Learning Guided by Signal Temporal Logic Specifications

Jiangwei Wang
Shuo Yang 0007
Ziyan An
Songyang Han
Zhili Zhang
Rahul Mangharam
Meiyi Ma
Fei Miao

Reward design is a key component of deep reinforcement learning (DRL), yet some tasks and designer’s objectives may be unnatural to define as a scalar cost function. Among the various techniques, formal methods integrated with DRL have garnered considerable attention due to their expressiveness and flexibility in defining the reward and requirements for different states and actions of the agent. Nevertheless, the exploration of leveraging Signal Temporal Logic (STL) for guiding multi-agent reinforcement learning (MARL) reward design is still limited. The presence of complex interactions, heterogeneous goals, and critical safety requirements in multi-agent systems exacerbates this challenge. In this paper, we propose a novel STL-guided multi-agent reinforcement learning framework. The STL requirements are designed to include both task specifications according to the objective of each agent and safety specifications. The robustness values from checking the states against STL specifications are leveraged to generate rewards. We validate our approach by conducting experiments across various testbeds. The experimental results demonstrate significant performance improvements compared to MARL without STL guidance, along with a remarkable increase in the overall safety rate of the multi-agent systems.

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ICRA Conference 2025 Conference Paper

Safety Guaranteed Robust Multi-Agent Reinforcement Learning with Hierarchical Control for Connected and Automated Vehicles

Zhili Zhang
H. M. Sabbir Ahmad
Ehsan Sabouni
Yanchao Sun
Furong Huang
Wenchao Li 0001
Fei Miao

We address the problem of coordination and control of Connected and Automated Vehicles (CAVs) in the presence of imperfect observations in mixed traffic environment. A commonly used approach is learning-based decision-making, such as reinforcement learning (RL). However, most existing safe RL methods suffer from two limitations: (i) they assume accurate state information, and (ii) safety is generally defined over the expectation of the trajectories. It remains challenging to design optimal coordination between multi-agents while ensuring hard safety constraints under system state uncertainties (e. g. , those that arise from noisy sensor measurements, communication, or state estimation methods) at every time step. We propose a safety guaranteed hierarchical coordination and control scheme called Safe-RMM to address the challenge. Specifically, the high-level coordination policy of CAVs in mixed traffic environment is trained by the Robust Multi-Agent Proximal Policy Optimization (RMAPPO) method. Though trained without uncertainty, our method leverages a worst-case Q network to ensure the model's robust performances when state uncertainties are present during testing. The low-level controller is implemented using model predictive control (MPC) with robust Control Barrier Functions (CBFs) to guarantee safety through their forward invariance property. We compare our method with baselines in different road networks in the CARLA simulator. Results show that our method provides the best evaluated safety and efficiency in challenging mixed traffic environments with uncertainties.

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IROS Conference 2025 Conference Paper

YOLO-MARL: You Only LLM Once for Multi-Agent Reinforcement Learning

Yuan Zhuang
Yi Shen
Zhili Zhang
Yuxiao Chen 0008
Fei Miao

Advancements in deep multi-agent reinforcement learning (MARL) have positioned it as a promising approach for decision-making in cooperative games. However, it still remains challenging for MARL agents to learn cooperative strategies for some game environments. Recently, large language models (LLMs) have demonstrated emergent reasoning capabilities, making them promising candidates for enhancing coordination among the agents. However, due to the model size of LLMs, it can be expensive to frequently infer LLMs for actions that agents can take. In this work, we propose You Only LLM Once for MARL (YOLO-MARL), a novel framework that leverages the high-level task planning capabilities of LLMs to improve the policy learning process of multi-agents in cooperative games. Notably, for each game environment, YOLO-MARL only requires one time interaction with LLMs in the proposed strategy generation, state interpretation and planning function generation modules, before the MARL policy training process. This avoids the ongoing costs and computational time associated with frequent LLMs API calls during training. Moreover, trained decentralized policies based on normal-sized neural networks operate independently of the LLM. We evaluate our method across two different environments and demonstrate that YOLO-MARL outperforms traditional MARL algorithms. The Github repository of our code can be found at https://github.com/paulzyzy/YOLO-MARL.

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ICML Conference 2024 Conference Paper

Constrained Reinforcement Learning Under Model Mismatch

Zhongchang Sun
Sihong He
Fei Miao
Shaofeng Zou

Existing studies on constrained reinforcement learning (RL) may obtain a well-performing policy in the training environment. However, when deployed in a real environment, it may easily violate constraints that were originally satisfied during training because there might be model mismatch between the training and real environments. To address this challenge, we formulate the problem as constrained RL under model uncertainty, where the goal is to learn a policy that optimizes the reward and at the same time satisfies the constraint under model mismatch. We develop a Robust Constrained Policy Optimization (RCPO) algorithm, which is the first algorithm that applies to large/continuous state space and has theoretical guarantees on worst-case reward improvement and constraint violation at each iteration during the training. We show the effectiveness of our algorithm on a set of RL tasks with constraints.

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ICML Conference 2024 Conference Paper

Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environments

Han Wang 0016
Sihong He
Zhili Zhang
Fei Miao
James Anderson 0001

We explore a Federated Reinforcement Learning (FRL) problem where $N$ agents collaboratively learn a common policy without sharing their trajectory data. To date, existing FRL work has primarily focused on agents operating in the same or “similar" environments. In contrast, our problem setup allows for arbitrarily large levels of environment heterogeneity. To obtain the optimal policy which maximizes the average performance across all potentially completely different environments, we propose two algorithms: FedSVRPG-M and FedHAPG-M. In contrast to existing results, we demonstrate that both FedSVRPG-M and FedHAPG-M, both of which leverage momentum mechanisms, can exactly converge to a stationary point of the average performance function, regardless of the magnitude of environment heterogeneity. Furthermore, by incorporating the benefits of variance-reduction techniques or Hessian approximation, both algorithms achieve state-of-the-art convergence results, characterized by a sample complexity of $\mathcal{O}\left(\epsilon^{-\frac{3}{2}}/N\right)$. Notably, our algorithms enjoy linear convergence speedups with respect to the number of agents, highlighting the benefit of collaboration among agents in finding a common policy.

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NeurIPS Conference 2024 Conference Paper

Policy Optimization for Robust Average Reward MDPs

Zhongchang Sun
Sihong He
Fei Miao
Shaofeng Zou

This paper studies first-order policy optimization for robust average cost Markov decision processes (MDPs). Specifically, we focus on ergodic Markov chains. For robust average cost MDPs, the goal is to optimize the worst-case average cost over an uncertainty set of transition kernels. We first develop a sub-gradient of the robust average cost. Based on the sub-gradient, a robust policy mirror descent approach is further proposed. To characterize its iteration complexity, we develop a lower bound on the difference of robust average cost between two policies and further show that the robust average cost satisfies the PL-condition. We then show that with increasing step size, our robust policy mirror descent achieves a linear convergence rate in the optimality gap, and with constant step size, our algorithm converges to an $\epsilon$-optimal policy with an iteration complexity of $\mathcal{O}(1/\epsilon)$. The convergence rate of our algorithm matches with the best convergence rate of policy-based algorithms for robust MDPs. Moreover, our algorithm is the first algorithm that converges to the global optimum with general uncertainty sets for robust average cost MDPs. We provide simulation results to demonstrate the performance of our algorithm.

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TMLR Journal 2024 Journal Article

What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?

Songyang Han
Sanbao Su
Sihong He
Shuo Han
Haizhao Yang
Shaofeng Zou
Fei Miao

Various methods for Multi-Agent Reinforcement Learning (MARL) have been developed with the assumption that agents' policies are based on accurate state information. However, policies learned through Deep Reinforcement Learning (DRL) are susceptible to adversarial state perturbation attacks. In this work, we propose a State-Adversarial Markov Game (SAMG) and make the first attempt to investigate different solution concepts of MARL under state uncertainties. Our analysis shows that the commonly used solution concepts of optimal agent policy and robust Nash equilibrium do not always exist in SAMGs. To circumvent this difficulty, we consider a new solution concept called robust agent policy, where agents aim to maximize the worst-case expected state value. We prove the existence of robust agent policy for finite state and finite action SAMGs. Additionally, we propose a Robust Multi-Agent Adversarial Actor-Critic (RMA3C) algorithm to learn robust policies for MARL agents under state uncertainties. Our experiments demonstrate that our algorithm outperforms existing methods when faced with state perturbations and greatly improves the robustness of MARL policies. Our code is public on https://songyanghan.github.io/what_is_solution/.

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IROS Conference 2023 Conference Paper

A Robust and Constrained Multi-Agent Reinforcement Learning Electric Vehicle Rebalancing Method in AMoD Systems

Sihong He
Yue Wang 0068
Shuo Han 0002
Shaofeng Zou
Fei Miao

Electric vehicles (EVs) play critical roles in autonomous mobility-on-demand (AMoD) systems, but their unique charging patterns increase the model uncertainties in AMoD systems (e. g. state transition probability). Since there usually exists a mismatch between the training and test/true environments, incorporating model uncertainty into system design is of critical importance in real-world applications. However, model uncertainties have not been considered explicitly in EV AMoD system rebalancing by existing literature yet, and the coexistence of model uncertainties and constraints that the decision should satisfy makes the problem even more challenging. In this work, we design a robust and constrained multi-agent reinforcement learning (MARL) framework with state transition kernel uncertainty for EV AMoD systems. We then propose a robust and constrained MARL algorithm (ROCOMA) with robust natural policy gradients (RNPG) that trains a robust EV rebalancing policy to balance the supply-demand ratio and the charging utilization rate across the city under model uncertainty. Experiments show that the ROCOMA can learn an effective and robust rebalancing policy. It outperforms non-robust MARL methods in the presence of model uncertainties. It increases the system fairness by 19. 6% and decreases the rebalancing costs by 75. 8%.

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IROS Conference 2023 Conference Paper

Privacy-Preserving and Uncertainty-Aware Federated Trajectory Prediction for Connected Autonomous Vehicles

Muzi Peng
Jiangwei Wang
Dongjin Song
Fei Miao
Lili Su

Deep learning is the method of choice for trajectory prediction for autonomous vehicles. Unfortunately, its data-hungry nature implicitly requires the availability of sufficiently rich and high-quality centralized datasets, which easily leads to privacy leakage. Besides, uncertainty-awareness becomes increasingly important for safety-crucial cyber physical systems whose prediction module heavily relies on machine learning tools. In this paper, we relax the data collection requirement and enhance uncertainty-awareness by using Federated Learning on Connected Autonomous Vehicles with an uncertainty-aware global objective. We name our algorithm as FLTP. We further introduce ALFLTP which boosts FLTP via using active learning techniques in adaptatively selecting participating clients. We consider two different metrics negative log-likelihood (NLL) and aleatoric uncertainty (AU) for client selection. Experiments on Argoverse dataset show that FLTP significantly outperforms the model trained on local data. In addition, ALFLTP-AU converges faster in training regression loss and performs better in terms of Miss Rate (MR) than FLTP in most rounds, and has more stable round-wise performance than ALFLTP-NLL.

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IROS Conference 2023 Conference Paper

Robust Electric Vehicle Balancing of Autonomous Mobility-on-Demand System: A Multi-Agent Reinforcement Learning Approach

Sihong He
Shuo Han 0002
Fei Miao

Electric autonomous vehicles (EAVs) are getting attention in future autonomous mobility-on-demand (AMoD) systems due to their economic and societal benefits. However, EAVs' unique charging patterns (long charging time, high charging frequency, unpredictable charging behaviors, etc.) make it challenging to accurately predict the EAVs supply in E-AMoD systems. Furthermore, the mobility demand's prediction uncertainty makes it an urgent and challenging task to design an integrated vehicle balancing solution under supply and demand uncertainties. Despite the success of reinforcement learning-based E-AMoD balancing algorithms, state uncertainties under the EV supply or mobility demand remain unexplored. In this work, we design a multi-agent reinforcement learning (MARL)-based framework for EAVs balancing in E-AMoD systems, with adversarial agents to model both the EAVs supply and mobility demand uncertainties that may undermine the vehicle balancing solutions. We then propose a robust E-AMoD Balancing MARL (REBAMA) algorithm to train a robust EAVs balancing policy to balance both the supply-demand ratio and charging utilization rate across the whole city. Experiments show that our proposed robust method performs better compared with a non-robust MARL method that does not consider state uncertainties; it improves the reward, charging utilization fairness, and supply-demand fairness by 19. 28%, 28. 18%, and 3. 97%, respectively. Compared with a robust optimization-based method, the proposed MARL algorithm can improve the reward, charging utilization fairness, and supply-demand fairness by 8. 21%, 8. 29%, and 9. 42%, respectively.

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TMLR Journal 2023 Journal Article

Robust Multi-Agent Reinforcement Learning with State Uncertainty

Sihong He
Songyang Han
Sanbao Su
Shuo Han
Shaofeng Zou
Fei Miao

In real-world multi-agent reinforcement learning (MARL) applications, agents may not have perfect state information (e.g., due to inaccurate measurement or malicious attacks), which challenges the robustness of agents' policies. Though robustness is getting important in MARL deployment, little prior work has studied state uncertainties in MARL, neither in problem formulation nor algorithm design. Motivated by this robustness issue and the lack of corresponding studies, we study the problem of MARL with state uncertainty in this work. We provide the first attempt to the theoretical and empirical analysis of this challenging problem. We first model the problem as a Markov Game with state perturbation adversaries (MG-SPA) by introducing a set of state perturbation adversaries into a Markov Game. We then introduce robust equilibrium (RE) as the solution concept of an MG-SPA. We conduct a fundamental analysis regarding MG-SPA such as giving conditions under which such a robust equilibrium exists. Then we propose a robust multi-agent Q-learning (RMAQ) algorithm to find such an equilibrium, with convergence guarantees. To handle high-dimensional state-action space, we design a robust multi-agent actor-critic (RMAAC) algorithm based on an analytical expression of the policy gradient derived in the paper. Our experiments show that the proposed RMAQ algorithm converges to the optimal value function; our RMAAC algorithm outperforms several MARL and robust MARL methods in multiple multi-agent environments when state uncertainty is present. The source code is public on https://github.com/sihongho/robust_marl_with_state_uncertainty.

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ICRA Conference 2023 Conference Paper

Spatial-Temporal-Aware Safe Multi-Agent Reinforcement Learning of Connected Autonomous Vehicles in Challenging Scenarios

Zhili Zhang
Songyang Han
Jiangwei Wang
Fei Miao

Communication technologies enable coordination among connected and autonomous vehicles (CAVs). However, it remains unclear how to utilize shared information to improve the safety and efficiency of the CAV system in dynamic and complicated driving scenarios. In this work, we propose a framework of constrained multi-agent reinforcement learning (MARL) with a parallel Safety Shield for CAVs in challenging driving scenarios that includes unconnected hazard vehicles. The coordination mechanisms of the proposed MARL include information sharing and cooperative policy learning, with Graph Convolutional Network (GCN)-Transformer as a spatial-temporal encoder that enhances the agent's environment awareness. The Safety Shield module with Control Barrier Functions (CBF)-based safety checking protects the agents from taking unsafe actions. We design a constrained multi-agent advantage actor-critic (CMAA2C) algorithm to train safe and cooperative policies for CAVs. With the experiment deployed in the CARLA simulator, we verify the performance of the safety checking, spatial-temporal encoder, and coordination mechanisms designed in our method by comparative experiments in several challenging scenarios with unconnected hazard vehicles. Results show that our proposed methodology significantly increases system safety and efficiency in challenging scenarios.

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ICRA Conference 2023 Conference Paper

Uncertainty Quantification of Collaborative Detection for Self-Driving

Sanbao Su
Yiming Li 0003
Sihong He
Songyang Han
Chen Feng 0002
Caiwen Ding
Fei Miao

Sharing information between connected and autonomous vehicles (CAVs) fundamentally improves the performance of collaborative object detection for self-driving. However, CAVs still have uncertainties on object detection due to practical challenges, which will affect the later modules in self-driving such as planning and control. Hence, uncertainty quantification is crucial for safety-critical systems such as CAVs. Our work is the first to estimate the uncertainty of collaborative object detection. We propose a novel uncertainty quantification method, called Double- M Quantification, which tailors a moving block bootstrap (MBB) algorithm with direct modeling of the multivariant Gaussian distribution of each corner of the bounding box. Our method captures both the epistemic uncertainty and aleatoric uncertainty with one inference pass based on the offline Double- M training process. And it can be used with different collaborative object detectors. Through experiments on the comprehensive collaborative perception dataset, we show that our Double-M method achieves more than 4× improvement on uncertainty score and more than 3% accuracy improvement, compared with the state-of-the-art uncertainty quantification methods. Our code is public on https://coperception.github.io/double-m-quantification/.

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ICRA Conference 2022 Conference Paper

Stable and Efficient Shapley Value-Based Reward Reallocation for Multi-Agent Reinforcement Learning of Autonomous Vehicles

Songyang Han
He Wang
Sanbao Su
Yuanyuan Shi
Fei Miao

With the development of sensing and communication technologies in networked cyber-physical systems (CPSs), multi-agent reinforcement learning (MARL)-based methodologies are integrated into the control process of physical systems and demonstrate prominent performance in a wide array of CPS domains, such as connected autonomous vehicles (CAVs). However, it remains challenging to mathematically characterize the improvement of the performance of CAVs with communication and cooperation capability. When each individual autonomous vehicle is originally self-interest, we can not assume that all agents would cooperate naturally during the training process. In this work, we propose to reallocate the system's total reward efficiently to motivate stable cooperation among autonomous vehicles. We formally define and quantify how to reallocate the system's total reward to each agent under the proposed transferable utility game, such that communication-based cooperation among multi-agents increases the system's total reward. We prove that Shapley value-based reward reallocation of MARL locates in the core if the transferable utility game is a convex game. Hence, the cooperation is stable and efficient and the agents should stay in the coalition or the cooperating group. We then propose a cooperative policy learning algorithm with Shapley value reward reallocation. In experiments, compared with several literature algorithms, we show the improvement of the mean episode system reward of CAV systems using our proposed algorithm.

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IROS Conference 2021 Conference Paper

Automated Type-Aware Traffic Speed Prediction based on Sparse Intelligent Camera System

Xiaoyang Xie
Kangjia Shao
Yang Wang 0015
Fei Miao
Desheng Zhang 0002

Many essential services for autonomous vehicles, e. g. , navigation on high-quality maps, are designed based on the understanding of traffic conditions, e. g. , travel time/speed on road segments, traffic flow, etc. However, most existing traffic condition models lack the consideration of the differentiation for vehicles with different types (e. g. , personal vehicles or trucks) and thus they cannot satisfy some type-specific services, e. g. , traffic-condition-based routing for autonomous vehicles with different types. To address this challenge, we design a novel vehicular mobility based sensing model called mDrive to predict the travel speed on the road segments, which is targeted for different types of vehicles by utilizing the camera data obtained from the traffic cameras equipped in the road intersections only, without any in-vehicle GPS devices. mDrive addresses the type-aware traffic speed prediction problem with sparse sensors based on three correlations: (1) the spatial correlation of travel speed on the connected road segments; (2) the temporal correlation of travel speed on the consecutive time slots; (3) the type correlation of different vehicular types’ speed on the same road segment. We implement mDrive on traffic camera data from the Chinese city Suzhou and evaluate it by using the detailed GPS data from personal vehicles, taxis, and trucks, with road contextual data as ground truth. The experiment show mDrive outperforms state-of-the-art methods by reducing 6. 2% mean relative error on average for all types of vehicles.

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IJCAI Conference 2021 Conference Paper

Enabling Retrain-free Deep Neural Network Pruning Using Surrogate Lagrangian Relaxation

Deniz Gurevin
Mikhail Bragin
Caiwen Ding
Shanglin Zhou
Lynn Pepin
Bingbing Li
Fei Miao

Network pruning is a widely used technique to reduce computation cost and model size for deep neural networks. However, the typical three-stage pipeline, i. e. , training, pruning and retraining (fine-tuning) significantly increases the overall training trails. In this paper, we develop a systematic weight-pruning optimization approach based on Surrogate Lagrangian relaxation (SLR), which is tailored to overcome difficulties caused by the discrete nature of the weight-pruning problem while ensuring fast convergence. We further accelerate the convergence of the SLR by using quadratic penalties. Model parameters obtained by SLR during the training phase are much closer to their optimal values as compared to those obtained by other state-of-the-art methods. We evaluate the proposed method on image classification tasks using CIFAR-10 and ImageNet, as well as object detection tasks using COCO 2014 and Ultra-Fast-Lane-Detection using TuSimple lane detection dataset. Experimental results demonstrate that our SLR-based weight-pruning optimization approach achieves higher compression rate than state-of-the-arts under the same accuracy requirement. It also achieves a high model accuracy even at the hard-pruning stage without retraining (reduces the traditional three-stage pruning to two-stage). Given a limited budget of retraining epochs, our approach quickly recovers the model accuracy.

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IROS Conference 2020 Conference Paper

Data-Driven Distributionally Robust Electric Vehicle Balancing for Mobility-on-Demand Systems under Demand and Supply Uncertainties

Sihong He
Lynn Pepin
Guang Wang 0001
Desheng Zhang 0002
Fei Miao

As electric vehicle (EV) technologies become mature, EV has been rapidly adopted in modern transportation systems, and is expected to provide future autonomous mobility-on-demand (AMoD) service with economic and societal benefits. However, EVs require frequent recharges due to their limited and unpredictable cruising ranges, and they have to be managed efficiently given the dynamic charging process. It is urgent and challenging to investigate a computationally efficient algorithm that provide EV AMoD system performance guarantees under model uncertainties, instead of using heuristic demand or charging models. To accomplish this goal, this work designs a data-driven distributionally robust optimization approach for vehicle supply-demand ratio and charging station utilization balancing, while minimizing the worst-case expected cost considering both passenger mobility demand uncertainties and EV supply uncertainties. We then derive an equivalent computationally tractable form for solving the distributionally robust problem in a computationally efficient way under ellipsoid uncertainty sets constructed from data. Based on E-taxi system data of Shenzhen city, we show that the average total balancing cost is reduced by 14. 49%, the average unfairness of supply-demand ratio and utilization is reduced by 15. 78% and 34. 51% respectively with the distributionally robust vehicle balancing method, compared with solutions which do not consider model uncertainties.

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IROS Conference 2017 Conference Paper

Artificial invariant subspace with potential functions for humanoid robot balancing

Xiang Deng
Fei Miao
Daniel D. Lee

Existing trajectory planning based locomotion algorithms lack the analytic tools to fully comprehend energy based movements that would allow for full stability and mobility. Such drawbacks make humanoid robots' locomotion sensitive to external disturbances and compromise robots' agility in unstructured environment. In this work, we specifically focus on the push recovery problem for humanoid robots. We propose an approach to design a nonlinear controller that is robust to external disturbances. It allows the state of the rigid body dynamics to asymptotically converge to the subspace that meet the criteria of balancing, based on the properties of artificial invariant subspace and potential functions. Our algorithm is completely adaptive in real time without requiring trajectory planning in advance. We demonstrate the robustness of the proposed algorithm base on extensive push recovery experiments on the DARWIN-OP robot platform on flat terrains.

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