Technical session talks from ICRA 2012
TechTalks from event: Technical session talks from ICRA 2012
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Data Based Learning
Improving the Efficiency of Bayesian Inverse Reinforcement LearningInverse reinforcement learning (IRL) is the task of learning the reward function of a Markov Decision Process (MDP) given knowledge of the transition function and a set of expert demonstrations. While many IRL algorithms exist, Bayesian IRL  provides a general and principled method of reward learning by casting the problem in the Bayesian inference framework. However, the algorithm as originally presented suffers from several inefficiencies that prohibit its use for even moderate problem sizes. This paper proposes modifications to the original Bayesian IRL algorithm to improve its efficiency and tractability in situations where the state space is large and the expert demonstrations span only a small portion of it. The key insight is that the inference task should be focused on states that are similar to those encountered by the expert, as opposed to making the naive assumption that the expert demonstrations contain enough information to accurately infer the reward function over the entire state space. A modified algorithm is presented and experimental results show substantially faster convergence while maintaining the solution quality of the original method.
Learning Diffeomorphisms Models of Robotic Sensorimotor CascadesThe problem of bootstrapping consists in designing agents that can learn from scratch the model of their sensorimotor cascade (the series of robot actuators, the external world, and the robot sensors) and use it to achieve useful tasks. In principle, we would want to design agents that can work for any robot dynamics and any robot sensor(s). One of the difficulties of this problem is the fact that the observations are very high dimensional, the dynamics is nonlinear, and there is a wide range of â€œrepresentation nuisancesâ€ to which we would want the agent to be robust. In this paper, we model the dynamics of sensorimotor cascades using diffeomorphisms of the sensel space. We show that this model captures the dynamics of camera and range-finder data, that it can be used for long-term predictions, and that it can capture nonlinear phenomena such as a limited field of view. Moreover, by analyzing the learned diffeomorphisms it is possible to recover the â€œlinear structureâ€ of the dynamics in a manner which is independent of the commands representation.
Interactive Generation of Dynamically Feasible Robot Trajectories from Sketches Using Temporal MimickingThis paper presents a method for generating dynamically-feasible, natural-looking robot motion from freehand sketches. Using trajectory optimization, it handles sketches that are too fast, jerky, or pass out of reach by enforcing the constraints of the robotâ€™s dynamic limitations while minimizing the relative temporal differences between the robotâ€™s trajectory and the sketch. To make the optimization fast enough for interactive use, a variety of enhancements are employed including decoupling the geometric and temporal optimizations and methods to select good initial trajectories. The technique is also applicable to transferring human motions onto robots with non-human appearance and dynamics, and we use our method to demonstrate a simulated humanoid imitating a golf swing as well as an industrial robot performing the motion of writing a cursive â€helloâ€ word.
A Robot Path Planning Framework That Learns from ExperienceWe propose a framework, called Lightning, for planning paths in high-dimensional spaces that is able to learn from experience, with the aim of reducing computation time. This framework is intended for manipulation tasks that arise in applications ranging from domestic assistance to robot-assisted surgery. Our framework consists of two main modules, which run in parallel: a planning-from-scratch module, and a module that retrieves and repairs paths stored in a path library. After a path is generated for a new query, a library manager decides whether to store the path based on computation time and the generated path's similarity to the retrieved path. To retrieve an appropriate path from the library we use two heuristics that exploit two key aspects of the problem: (i) A correlation between the amount a path violates constraints and the amount of time needed to repair that path, and (ii) the implicit division of constraints into those that vary across environments in which the robot operates and those that do not. We evaluated an implementation of the framework on several tasks for the PR2 mobile manipulator and a minimally-invasive surgery robot in simulation. We found that the retrieve-and-repair module produced paths faster than planning-from-scratch in over 90% of test cases for the PR2 and in 58% of test cases for the minimally-invasive surgery robot.
Evaluation of Commonsense Knowledge for Intuitive Robotic ServiceHuman commonsense is required to improve quality of robotic application. However, to acquire the necessary knowledge, robot needs to evaluate the appropriateness of the data it has collected. This paper presents an evaluation method, by combining the weighting mechanism in commonsense databases with a set of weighting factors. The method was verified on our Basic-level Knowledge Network. We conducted questionnaire to collect a commonsense data set and estimate weighting factors. Result showed that, the proposed method was able to build Robot Technology (RT) Ontology for a smart â€œBring somethingâ€ robotic service. More importantly, it allowed robot to learn new knowledge when necessary. An intuitive human-robot interface application was developed as an example base on our approach.
A Temporal Bayesian Network with Application to Design of a Proactive Robotic AssistantFor effective human-robot interaction, a robot should be able to make prediction about future circumstance. This enables the robot to generate preparative behaviors to reduce waiting time, thereby greatly improving the quality of the interaction. In this paper, we propose a novel probabilistic temporal prediction method for proactive interaction that is based on a Bayesian network approach. In our proposed method, conditional probabilities of temporal events can be explicitly represented by defining temporal nodes in a Bayesian network. Utilizing these nodes, both temporal and causal information can be simultaneously inferred in a unified framework. An assistant robot can use the temporal Bayesian network to infer the best proactive action and the best time to act so that the waiting time for both the human and the robot is minimized. To validate our proposed method, we present experimental results for case in which a robot assists in a human assembly task.