Simmons, Reid


Plan-Time Multi-Model Switching for Motion Planning

AAAI Conferences

Robot navigation through non-uniform environments requires reliable motion plan generation. The choice of planning model fidelity can significantly impact performance. Prior research has shown that reducing model fidelity saves planning time, but sacrifices execution reliability. While current adaptive hierarchical motion planning techniques are promising, we present a framework that leverages a richer set of robot motion models at plan-time. The framework chooses when to switch models and what model is most applicable within a single trajectory. For instance, more complex environment locales require higher fidelity models, while lower fidelity models are sufficient for simpler parts of the planning space, thus saving plan time. Our algorithm continuously aims to pick the model that best handles the current local environment. This effectively generates a single, mixed-fidelity plan. We present results for a simulated mobile robot with attached trailer in a hospital domain. We compare using a single motion planning model to switching with our framework of multiple models. Our results demonstrate that multi-fidelity model switching increases plan-time efficiency without sacrificing execution reliability.


Online Learning of Robot Soccer Free Kick Plans Using a Bandit Approach

AAAI Conferences

This paper presents an online learning approach for teams of autonomous soccer robots to select free kick plans. In robot soccer, free kicks present an opportunity to execute plans with relatively controllable initial conditions. However, the effectiveness of each plan is highly dependent on the adversary, and there are few free kicks during each game, making it necessary to learn online from sparse observations. To achieve learning, we first greatly reduce the planning space by framing the problem as a contextual multi-armed bandit problem, in which the actions are a set of pre-computed plans, and the state is the position of the free kick on the field. During execution, we model the reward function for different free kicks using Gaussian Processes, and perform online learning using the Upper Confidence Bound algorithm. Results from a physics-based simulation reveal that the robots are capable of adapting to various different realistic opponents to maximize their expected reward during free kicks.


Intention-Aware Multi-Human Tracking for Human-Robot Interaction via Particle Filtering over Sets

AAAI Conferences

In order to successfully interact with multiple humans in social situations, an intelligent robot should have the ability to track multi-humans, and understand their motion intentions. We formalize this problem as a hidden Markov model, and estimate the posterior densities by particle filtering over sets approach. Our approach avoids directly performing observation-to-target association by defining a set as a joint state. The human identification problem is then solved in an expectation-maximization way. We evaluate the effectiveness of our approach by both benchamark test and real robot experiments.


Heuristic Search Value Iteration for POMDPs

arXiv.org Artificial Intelligence

We present a novel POMDP planning algorithm called heuristic search value iteration (HSVI).HSVI is an anytime algorithm that returns a policy and a provable bound on its regret with respect to the optimal policy. HSVI gets its power by combining two well-known techniques: attention-focusing search heuristics and piecewise linear convex representations of the value function. HSVI's soundness and convergence have been proven. On some benchmark problems from the literature, HSVI displays speedups of greater than 100 with respect to other state-of-the-art POMDP value iteration algorithms. We also apply HSVI to a new rover exploration problem 10 times larger than most POMDP problems in the literature.


Risk-Variant Policy Switching to Exceed Reward Thresholds

AAAI Conferences

This paper presents a decision-theoretic planning approach for probabilistic environments where the agent's goal is to win, which we model as maximizing the probability of being above a given reward threshold. In competitive domains, second is as good as last, and it is often desirable to take risks if one is in danger of losing, even if the risk does not pay off very often. Our algorithm maximizes the probability of being above a particular reward threshold by dynamically switching between a suite of policies, each of which encodes a different level of risk. This method does not explicitly encode time or reward into the state space, and decides when to switch between policies during each execution step. We compare a risk-neutral policy to switching among different risk-sensitive policies, and show that our approach improves the agent's probability of winning.


Believable Robot Characters

AI Magazine

Believability of characters has been an objective in literature, theater, film, and animation. We argue that believable robot characters are important in human-robot interaction, as well. In particular, we contend that believable characters evoke users' social responses that, for some tasks, lead to more natural interactions and are associated with improved task performance. In a dialogue-capable robot, a key to such believability is the integration of a consistent storyline, verbal and nonverbal behaviors, and sociocultural context.


Believable Robot Characters

AI Magazine

Believability of characters has been an objective in literature, theater, film, and animation. We argue that believable robot characters are important in human-robot interaction, as well. In particular, we contend that believable characters evoke users’ social responses that, for some tasks, lead to more natural interactions and are associated with improved task performance. In a dialogue-capable robot, a key to such believability is the integration of a consistent storyline, verbal and nonverbal behaviors, and sociocultural context. We describe our work in this area and present empirical results from three robot receptionist testbeds that operate "in the wild."


Do You Really Want to Know? Display Questions in Human-Robot Dialogues. A Position Paper

AAAI Conferences

Not all questions are asked with the same intention. Humans tend to address the implicit meaning of the question (that contributes to its pragmatic force), which requires knowledge of the context and a degree of common ground, more so than addressing the explicit propositional content of the question. Is recognizing the pragmatic force in today's human-robot dialogue systems worth the trouble? We focus on display questions (questions to which the asker already knows the answer) and argue that there are realistic human-robot interaction scenarios in existence today that would benefit from the deeper intention recognition. We also propose a method for obtaining display question annotations by embedding an elicitation question into the dialogue. The preliminary study of our robot receptionist shows that at least 16.7% of interactions with the embedded elicitation question include a display question.


GRACE: An Autonomous Robot for the AAAI Robot Challenge

AI Magazine

In an attempt to solve as much of the AAAI Robot Challenge as possible, five research institutions representing academia, industry, and government integrated their research into a single robot named GRACE. This article describes this first-year effort by the GRACE team, including not only the various techniques each participant brought to GRACE but also the difficult integration effort itself.


GRACE: An Autonomous Robot for the AAAI Robot Challenge

AI Magazine

In an attempt to solve as much of the AAAI Robot Challenge as possible, five research institutions representing academia, industry, and government integrated their research into a single robot named GRACE. This article describes this first-year effort by the GRACE team, including not only the various techniques each participant brought to GRACE but also the difficult integration effort itself.