Learning Task Specifications from Demonstrations

Real world applications often naturally decompose into several sub-tasks. In many settings (e.g., robotics) demonstrations provide a natural way to specify the sub-tasks. However, most methods for learning from demonstrations either do not provide guarantees that the artifacts learned for the sub-tasks can be safely recombined or limit the types of composition available. Motivated by this deficit, we consider the problem of inferring Boolean non-Markovian rewards (also known as logical trace properties or specifications) from demonstrations provided by an agent operating in an uncertain, stochastic environment. Crucially, specifications admit well-defined composition rules that are typically easy to interpret. In this paper, we formulate the specification inference task as a maximum a posteriori (MAP) probability inference problem, apply the principle of maximum entropy to derive an analytic demonstration likelihood model and give an efficient approach to search for the most likely specification in a large candidate pool of specifications. In our experiments, we demonstrate how learning specifications can help avoid common problems that often arise due to ad-hoc reward composition.Comment: NIPS 201

Efficiently Finding Approximately-Optimal Queries for Improving Policies and Guaranteeing Safety

When a computational agent (called the “robot”) takes actions on behalf of a human user, it may be uncertain about the human’s preferences. The human may initially specify her preferences incompletely or inaccurately. In this case, the robot’s performance may be unsatisfactory or even cause negative side effects to the environment. There are approaches in the literature that may solve this problem. For example, the human can provide some demonstrations which clarify the robot’s uncertainty. The human may give real-time feedback to the robot’s behavior, or monitor the robot and stop the robot when it may perform anything dangerous. However, these methods typically require much of the human’s attention. Alternatively, the robot may estimate the human’s true preferences using the specified preferences, but this is error-prone and requires making assumptions on how the human specifies her preferences. In this thesis, I consider a querying approach. Before taking any actions, the robot has a chance to query the human about her preferences. For example, the robot may query the human about which trajectory in a set of trajectories she likes the most, or whether the human cares about some side effects to the domain. After the human responds to the query, the robot expects to improve its performance and/or guarantee that its behavior is considered safe by the human. If we do not impose any constraint on the number of queries the robot can pose, the robot may keep posing queries until it is absolutely certain about the human’s preferences. This may consume too much of the human’s cognitive load. The information obtained in the responses to some of the queries may only marginally improve the robot’s performance, which is not worth the human’s attention at all. So in the problems considered in this thesis, I constrain the number of queries that the robot can pose, or associate each query with a cost. The research question is how to efficiently find the most useful query under such constraints. Finding a provably optimal query can be challenging since it is usually a combinatorial optimization problem. In this thesis, I contribute to providing efficient query selection algorithms under uncertainty. I first formulate the robot’s uncertainty as reward uncertainty and safety-constraint uncertainty. Under only reward uncertainty, I provide a query selection algorithm that finds approximately-optimal k-response queries. Under only safety-constraint uncertainty, I provide a query selection algorithm that finds an optimal k-element query to improve a known safe policy, and an algorithm that uses a set-cover-based query selection strategy to find an initial safe policy. Under both types of uncertainty simultaneously, I provide a batch-query-based querying method that empirically outperforms other baseline querying methods.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163125/1/shunzh_1.pd

Closed-loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search

In search applications, autonomous unmanned vehicles must be able to efficiently reacquire and localize mobile targets that can remain out of view for long periods of time in large spaces. As such, all available information sources must be actively leveraged -- including imprecise but readily available semantic observations provided by humans. To achieve this, this work develops and validates a novel collaborative human-machine sensing solution for dynamic target search. Our approach uses continuous partially observable Markov decision process (CPOMDP) planning to generate vehicle trajectories that optimally exploit imperfect detection data from onboard sensors, as well as semantic natural language observations that can be specifically requested from human sensors. The key innovation is a scalable hierarchical Gaussian mixture model formulation for efficiently solving CPOMDPs with semantic observations in continuous dynamic state spaces. The approach is demonstrated and validated with a real human-robot team engaged in dynamic indoor target search and capture scenarios on a custom testbed.Comment: Final version accepted and submitted to 2018 FUSION Conference (Cambridge, UK, July 2018

Reinforcement learning with limited reinforcement: Using Bayes risk for active learning in POMDPs

Acting in domains where an agent must plan several steps ahead to achieve a goal can be a challenging task, especially if the agentʼs sensors provide only noisy or partial information. In this setting, Partially Observable Markov Decision Processes (POMDPs) provide a planning framework that optimally trades between actions that contribute to the agentʼs knowledge and actions that increase the agentʼs immediate reward. However, the task of specifying the POMDPʼs parameters is often onerous. In particular, setting the immediate rewards to achieve a desired balance between information-gathering and acting is often not intuitive. In this work, we propose an approximation based on minimizing the immediate Bayes risk for choosing actions when transition, observation, and reward models are uncertain. The Bayes-risk criterion avoids the computational intractability of solving a POMDP with a multi-dimensional continuous state space; we show it performs well in a variety of problems. We use policy queries—in which we ask an expert for the correct action—to infer the consequences of a potential pitfall without experiencing its effects. More important for human–robot interaction settings, policy queries allow the agent to learn the reward model without the reward values ever being specified

Strategy Synthesis for Autonomous Agents Using PRISM

We present probabilistic models for autonomous agent search and retrieve missions derived from Simulink models for an Unmanned Aerial Vehicle (UAV) and show how probabilistic model checking and the probabilistic model checker PRISM can be used for optimal controller generation. We introduce a sequence of scenarios relevant to UAVs and other autonomous agents such as underwater and ground vehicles. For each scenario we demonstrate how it can be modelled using the PRISM language, give model checking statistics and present the synthesised optimal controllers. We conclude with a discussion of the limitations when using probabilistic model checking and PRISM in this context and what steps can be taken to overcome them. In addition, we consider how the controllers can be returned to the UAV and adapted for use on larger search areas