The increasing presence of robots in industries has not gone unnoticed. Large industrial players have incorporated them into their production lines, but smaller companies hesitate due to high initial costs and the lack of programming expertise. In this work we introduce a framework that combines two disciplines, Programming by Demonstration and Automated Planning, to allow users without any programming knowledge to program a robot. The user teaches the robot atomic actions together with their semantic meaning and represents them in terms of preconditions and effects. Using these atomic actions the robot can generate action sequences autonomously to reach any goal given by the user. We evaluated the usability of our framework in terms of user experiments with a Baxter Research Robot and showed that it is well-adapted to users without any programming experience.
Our work uses the policy derivation technique of plans, where the policy is represented as a sequence of actions leading from an initial state to a final goal state. Actions are defined in terms of preconditions, i.e. a state of the world that must be attained in order to execute the action, and effects, i.e. the expected state resulting from the action execution. Existing implementations of plans learn action sequences to achieve a single goal starting from different initial states @cite_6 , but do not learn different action models that can be applied independently. Our approach teaches the robot independent action models that can be used by automated planning techniques to reach any given goal.
@cite_6
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We present a novel and scalable policy synthesis approach for robots. Rather than producing single-path plans for a static environment, we consider changing environments with uncontrollable agents, where the robot needs a policy to respond correctly over the infinite-horizon interaction with the environment. Our approach operates on task and motion domains, and combines actions over discrete states with continuous, collision-free paths. We synthesize a task and motion policy by iteratively generating a candidate policy and verifying its correctness. For efficient policy generation, we use grammars for potential policies to limit the search space and apply domain-specific heuristics to generalize verification failures, providing stricter constraints on policy candidates. For efficient policy verification, we construct compact, symbolic constraints for valid policies and employ a Satisfiability Modulo Theories (SMT) solver to check the validity of these constraints. Furthermore, the SMT solver enables quantitative specifications such as energy limits. The results show that our approach offers better scalability compared to a state-of-the-art policy synthesis tool in the tested benchmarks and demonstrate an order-of-magnitude speedup from our heuristics for the tested mobile manipulation domain. Imitation learning of sequential, goal-directed behavior by standard supervised techniques is often difficult. We frame learning such behaviors as a maximum margin structured prediction problem over a space of policies. In this approach, we learn mappings from features to cost so an optimal policy in an MDP with these cost mimics the expert's behavior. Further, we demonstrate a simple, provably efficient approach to structured maximum margin learning, based on the subgradient method, that leverages existing fast algorithms for inference. Although the technique is general, it is particularly relevant in problems where A* and dynamic programming approaches make learning policies tractable in problems beyond the limitations of a QP formulation. We demonstrate our approach applied to route planning for outdoor mobile robots, where the behavior a designer wishes a planner to execute is often clear, while specifying cost functions that engender this behavior is a much more difficult task. We provide a method, based on the theory of Markov decision processes, for efficient planning in stochastic domains. Goals are encoded as reward functions, expressing the desirability of each world state; the planner must find a policy (mapping from states to actions) that maximizes future rewards. Standard goals of achievement, as well as goals of maintenance and prioritized combinations of goals, can be specified in this way. An optimal policy can be found using existing methods, but these methods require time at best polynomial in the number of states in the domain, where the number of states is exponential in the number of propositions (or state variables). By using information about the starting state, the reward function, and the transition probabilities of the domain, we restrict the planner''s attention to a set of world states that are likely to be encountered in satisfying the goal. Using this restricted set of states, the planner can generate more or less complete plans depending on the time it has available. Our approach employs several iterative refinement routines for solving different aspects of the decision making problem. We describe the meta-level control problem of deliberation scheduling , allocating computational resources to these routines. We provide different models corresponding to optimization problems that capture the different circumstances and computational strategies for decision making under time constraints. We consider precursor models in which all decision making is performed prior to execution and recurrent models in which decision making is performed in parallel with execution, accounting for the states observed during execution and anticipating future states. We describe experimental results for both the precursor and recurrent problems that demonstrate planning times that grow slowly as a function of domain size. We consider the problem of using real-time feedback from contact sensors to create closed-loop pushing actions. To do so, we formulate the problem as a partially observable Markov decision process POMDP with a transition model based on a physics simulator and a reward function that drives the robot towards a successful grasp. We demonstrate that it is intractable to solve the full POMDP with traditional techniques and introduce a novel decomposition of the policy into pre- and post-contact stages to reduce the computational complexity. Our method uses an offline point-based solver on a variable-resolution discretization of the state space to solve for a post-contact policy as a pre-computation step. Then, at runtime, we use an A* search to compute a pre-contact trajectory. We prove that the value of the resulting policy is within a bound of the value of the optimal policy and give intuition about when it performs well. Additionally, we show the policy produced by our algorithm achieves a successful grasp more quickly and with higher probability than a baseline QMDP policy on two different objects in simulation. Finally, we validate our simulation results on a real robot using commercially available tactile sensors.