As robotic systems move from highly structured environments to open worlds, incorporating uncertainty from dynamics learning or state estimation into the control pipeline is essential for robust performance. In this paper we present a nonlinear particle model predictive control (PMPC) approach to control under uncertainty, which directly incorporates any particle-based uncertainty representation, such as those common in robotics. Our approach builds on scenario methods for MPC, but in contrast to existing approaches, which either constrain all or only the first timestep to share actions across scenarios, we investigate the impact of a \textit{partial consensus horizon}. Implementing this optimization for nonlinear dynamics by leveraging sequential convex optimization, our approach yields an efficient framework that can be tuned to the particular information gain dynamics of a system to mitigate both over-conservatism and over-optimism. We investigate our approach for two robotic systems across three problem settings: time-varying, partially observed dynamics; sensing uncertainty; and model-based reinforcement learning, and show that our approach improves performance over baselines in all settings.

0
下载
关闭预览

相关内容

最优化是应用数学的一个分支,主要指在一定条件限制下,选取某种研究方案使目标达到最优的一种方法。最优化问题在当今的军事、工程、管理等领域有着极其广泛的应用。

Flexible manufacturing in the process industry requires control systems to achieve time-varying setpoints (e.g., product specifications) based on market demand. Contraction theory provides a useful framework for reference-independent system analysis and tracking control for nonlinear systems. However, determination of the control contraction metrics and control laws can be very difficult for general nonlinear systems. This work develops an approach to discrete-time contraction analysis and control using neural networks. The methodology involves training a neural network to learn a contraction metric and feedback gain. The resulting contraction-based controller embeds the trained neural network and is capable of achieving efficient tracking of time-varying references, with a full range of model uncertainty, without the need for controller structure redesign. This is a robust approach that can deal with bounded parametric uncertainties in the process model, which are commonly encountered in industrial (chemical) processes. Simulation examples are provided to illustrate the above approach.

0
0
下载
预览

A self-learning adaptive system (SLAS) uses machine learning to enable and enhance its adaptability. Such systems are expected to perform well in dynamic situations. For learning high-performance adaptation policy, some assumptions must be made on the environment-system dynamics when information about the real situation is incomplete. However, these assumptions cannot be expected to be always correct, and yet it is difficult to enumerate all possible assumptions. This leads to the problem of incomplete-information learning. We consider this problem as multiple model problem in terms of finding the adaptation policy that can cope with multiple models of environment-system dynamics. This paper proposes a novel approach to engineering the online adaptation of SLAS. It separates three concerns that are related to the adaptation policy and presents the modeling and synthesis process, with the goal of achieving higher model construction efficiency. In addition, it designs a meta-reinforcement learning algorithm for learning the meta policy over the multiple models, so that the meta policy can quickly adapt to the real environment-system dynamics. At last, it reports the case study on a robotic system to evaluate the adaptability of the approach.

0
0
下载
预览

Multi-objective controller synthesis concerns the problem of computing an optimal controller subject to multiple (possibly conflicting) objective properties. The relative importance of objectives is often specified by human decision-makers. However, there is inherent uncertainty in human preferences (e.g., due to different preference elicitation methods). In this paper, we formalize the notion of uncertain human preferences and present a novel approach that accounts for uncertain human preferences in the multi-objective controller synthesis for Markov decision processes (MDPs). Our approach is based on mixed-integer linear programming (MILP) and synthesizes a sound, optimally permissive multi-strategy with respect to a multi-objective property and an uncertain set of human preferences. Experimental results on a range of large case studies show that our MILP-based approach is feasible and scalable to synthesize sound, optimally permissive multi-strategies with varying MDP model sizes and uncertainty levels of human preferences. Evaluation via an online user study also demonstrates the quality and benefits of synthesized (multi-)strategies.

0
0
下载
预览

The combination of policy search and deep neural networks holds the promise of automating a variety of decision-making tasks. Model Predictive Control (MPC) provides robust solutions to robot control tasks by making use of a dynamical model of the system and solving an optimization problem online over a short planning horizon. In this work, we leverage probabilistic decision-making approaches and the generalization capability of artificial neural networks to the powerful online optimization by learning a deep high-level policy for the MPC (High-MPC). Conditioning on robot's local observations, the trained neural network policy is capable of adaptively selecting high-level decision variables for the low-level MPC controller, which then generates optimal control commands for the robot. First, we formulate the search of high-level decision variables for MPC as a policy search problem, specifically, a probabilistic inference problem. The problem can be solved in a closed-form solution. Second, we propose a self-supervised learning algorithm for learning a neural network high-level policy, which is useful for online hyperparameter adaptations in highly dynamic environments. We demonstrate the importance of incorporating the online adaption into autonomous robots by using the proposed method to solve a challenging control problem, where the task is to control a simulated quadrotor to fly through a swinging gate. We show that our approach can handle situations that are difficult for standard MPC.

0
0
下载
预览

Sequential decision-making under cost-sensitive tasks is prohibitively daunting, especially for the problem that has a significant impact on people's daily lives, such as malaria control, treatment recommendation. The main challenge faced by policymakers is to learn a policy from scratch by interacting with a complex environment in a few trials. This work introduces a practical, data-efficient policy learning method, named Variance-Bonus Monte Carlo Tree Search~(VB-MCTS), which can copy with very little data and facilitate learning from scratch in only a few trials. Specifically, the solution is a model-based reinforcement learning method. To avoid model bias, we apply Gaussian Process~(GP) regression to estimate the transitions explicitly. With the GP world model, we propose a variance-bonus reward to measure the uncertainty about the world. Adding the reward to the planning with MCTS can result in more efficient and effective exploration. Furthermore, the derived polynomial sample complexity indicates that VB-MCTS is sample efficient. Finally, outstanding performance on a competitive world-level RL competition and extensive experimental results verify its advantage over the state-of-the-art on the challenging malaria control task.

0
0
下载
预览

We present an approach to learn an object-centric forward model, and show that this allows us to plan for sequences of actions to achieve distant desired goals. We propose to model a scene as a collection of objects, each with an explicit spatial location and implicit visual feature, and learn to model the effects of actions using random interaction data. Our model allows capturing the robot-object and object-object interactions, and leads to more sample-efficient and accurate predictions. We show that this learned model can be leveraged to search for action sequences that lead to desired goal configurations, and that in conjunction with a learned correction module, this allows for robust closed loop execution. We present experiments both in simulation and the real world, and show that our approach improves over alternate implicit or pixel-space forward models. Please see our project page (https://judyye.github.io/ocmpc/) for result videos.

0
4
下载
预览

We present Residual Policy Learning (RPL): a simple method for improving nondifferentiable policies using model-free deep reinforcement learning. RPL thrives in complex robotic manipulation tasks where good but imperfect controllers are available. In these tasks, reinforcement learning from scratch remains data-inefficient or intractable, but learning a residual on top of the initial controller can yield substantial improvement. We study RPL in five challenging MuJoCo tasks involving partial observability, sensor noise, model misspecification, and controller miscalibration. By combining learning with control algorithms, RPL can perform long-horizon, sparse-reward tasks for which reinforcement learning alone fails. Moreover, we find that RPL consistently and substantially improves on the initial controllers. We argue that RPL is a promising approach for combining the complementary strengths of deep reinforcement learning and robotic control, pushing the boundaries of what either can achieve independently.

0
3
下载
预览

Autonomous urban driving navigation with complex multi-agent dynamics is under-explored due to the difficulty of learning an optimal driving policy. The traditional modular pipeline heavily relies on hand-designed rules and the pre-processing perception system while the supervised learning-based models are limited by the accessibility of extensive human experience. We present a general and principled Controllable Imitative Reinforcement Learning (CIRL) approach which successfully makes the driving agent achieve higher success rates based on only vision inputs in a high-fidelity car simulator. To alleviate the low exploration efficiency for large continuous action space that often prohibits the use of classical RL on challenging real tasks, our CIRL explores over a reasonably constrained action space guided by encoded experiences that imitate human demonstrations, building upon Deep Deterministic Policy Gradient (DDPG). Moreover, we propose to specialize adaptive policies and steering-angle reward designs for different control signals (i.e. follow, straight, turn right, turn left) based on the shared representations to improve the model capability in tackling with diverse cases. Extensive experiments on CARLA driving benchmark demonstrate that CIRL substantially outperforms all previous methods in terms of the percentage of successfully completed episodes on a variety of goal-directed driving tasks. We also show its superior generalization capability in unseen environments. To our knowledge, this is the first successful case of the learned driving policy through reinforcement learning in the high-fidelity simulator, which performs better-than supervised imitation learning.

0
4
下载
预览

Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and unexpected perturbations can cause proficient but narrowly-learned policies to fail at test time. In this work, we propose to learn how to quickly and effectively adapt online to new situations as well as to perturbations. To enable sample-efficient meta-learning, we consider learning online adaptation in the context of model-based reinforcement learning. Our approach trains a global model such that, when combined with recent data, the model can be be rapidly adapted to the local context. Our experiments demonstrate that our approach can enable simulated agents to adapt their behavior online to novel terrains, to a crippled leg, and in highly-dynamic environments.

0
7
下载
预览

This paper presents a safety-aware learning framework that employs an adaptive model learning method together with barrier certificates for systems with possibly nonstationary agent dynamics. To extract the dynamic structure of the model, we use a sparse optimization technique, and the resulting model will be used in combination with control barrier certificates which constrain feedback controllers only when safety is about to be violated. Under some mild assumptions, solutions to the constrained feedback-controller optimization are guaranteed to be globally optimal, and the monotonic improvement of a feedback controller is thus ensured. In addition, we reformulate the (action-)value function approximation to make any kernel-based nonlinear function estimation method applicable. We then employ a state-of-the-art kernel adaptive filtering technique for the (action-)value function approximation. The resulting framework is verified experimentally on a brushbot, whose dynamics is unknown and highly complex.

0
3
下载
预览
小贴士
相关论文
Mingyue Zhang,Jialong Li,Haiyan Zhao,Kenji Tei,Shinichi Honiden,Zhi Jin
0+阅读 · 5月11日
Shenghui Chen,Kayla Boggess,David Parker,Lu Feng
0+阅读 · 5月10日
Yunlong Song,Davide Scaramuzza
0+阅读 · 5月9日
Lixin Zou,Long Xia,Linfang Hou,Xiangyu Zhao,Dawei Yin
0+阅读 · 5月5日
Object-centric Forward Modeling for Model Predictive Control
Yufei Ye,Dhiraj Gandhi,Abhinav Gupta,Shubham Tulsiani
4+阅读 · 2019年10月8日
Residual Policy Learning
Tom Silver,Kelsey Allen,Josh Tenenbaum,Leslie Kaelbling
3+阅读 · 2018年12月15日
CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving
Xiaodan Liang,Tairui Wang,Luona Yang,Eric Xing
4+阅读 · 2018年7月10日
Ignasi Clavera,Anusha Nagabandi,Ronald S. Fearing,Pieter Abbeel,Sergey Levine,Chelsea Finn
7+阅读 · 2018年3月30日
Motoya Ohnishi,Li Wang,Gennaro Notomista,Magnus Egerstedt
3+阅读 · 2018年1月29日
相关VIP内容
专知会员服务
35+阅读 · 2020年7月26日
专知会员服务
39+阅读 · 2020年7月4日
因果图,Causal Graphs,52页ppt
专知会员服务
123+阅读 · 2020年4月19日
专知会员服务
79+阅读 · 2020年2月1日
Stabilizing Transformers for Reinforcement Learning
专知会员服务
22+阅读 · 2019年10月17日
强化学习最新教程,17页pdf
专知会员服务
55+阅读 · 2019年10月11日
【哈佛大学商学院课程Fall 2019】机器学习可解释性
专知会员服务
43+阅读 · 2019年10月9日
相关资讯
ICRA 2019 论文速览 | 基于Deep Learning 的SLAM
计算机视觉life
37+阅读 · 2019年7月22日
Hierarchically Structured Meta-learning
CreateAMind
9+阅读 · 2019年5月22日
Transferring Knowledge across Learning Processes
CreateAMind
6+阅读 · 2019年5月18日
已删除
将门创投
7+阅读 · 2019年3月28日
Unsupervised Learning via Meta-Learning
CreateAMind
26+阅读 · 2019年1月3日
meta learning 17年:MAML SNAIL
CreateAMind
8+阅读 · 2019年1月2日
A Technical Overview of AI & ML in 2018 & Trends for 2019
待字闺中
10+阅读 · 2018年12月24日
Hierarchical Imitation - Reinforcement Learning
CreateAMind
15+阅读 · 2018年5月25日
Reinforcement Learning: An Introduction 2018第二版 500页
CreateAMind
9+阅读 · 2018年4月27日
强化学习族谱
CreateAMind
10+阅读 · 2017年8月2日
Top