Learning from a Single Demonstration: Motion Planning with Skill Segmentation
نویسندگان
چکیده
We propose an approach to control learning from demonstration that first segments demonstration trajectories to identify subgoals, then uses model-based control methods to sequentially reach these subgoals to solve the overall task. Using this approach, we show that a mobile robot is able to solve a combined navigation and manipulation task robustly after observing only a single successful trajectory.
منابع مشابه
Towards Robust Skill Generalization: Unifying Learning from Demonstration and Motion Planning
In this paper, we present Combined Learning from demonstration And Motion Planning (CLAMP) as an efficient approach to skill learning and generalizable skill reproduction. CLAMP combines the strengths of Learning from Demonstration (LfD) and motion planning into a unifying framework. We carry out probabilistic inference to find trajectories which are optimal with respect to a given skill and al...
متن کاملSkill Generalization via Inference-based Planning
We present a novel approach which unifies conventional learning from demonstration (LfD) and motion planning using probabilistic inference, for generalizable skill reproduction. As a part of this approach, we also present a new probabilistic skill model that requires minimal parameter tuning, and is more suited for encoding skill constraints and performing inference in an efficient manner. Prel...
متن کاملCombining Gaussian Processes and Conventional Path Planning in a Learning from Demonstration Framework
Today, robots are already able to solve specific tasks in laboratory environments. Since everyday environments are more complex, the robot skills required to solve everyday tasks cannot be known in advance and thus not be programmed beforehand. Rather, the robot must be able to learn those tasks being instructed by users without any technical background. Hence, Learning from Demonstration (LfD)...
متن کاملSimultaneous Learning of Hierarchy and Primitives for Complex Robot Tasks
We present a new interaction paradigm for robot learning from demonstration, called simultaneous learning of hierarchy and primitives (SLHAP), in which information about hierarchy and primitives is naturally interleaved in a single, coherent demonstration session. A key innovation in the new paradigm is the human demonstrator’s narration of primitives as he executes them, which allows the syste...
متن کاملConstructing Skill Trees for Reinforcement Learning Agents from Demonstration Trajectories
We introduce CST, an algorithm for constructing skill trees from demonstration trajectories in continuous reinforcement learning domains. CST uses a changepoint detection method to segment each trajectory into a skill chain by detecting a change of appropriate abstraction, or that a segment is too complex to model as a single skill. The skill chains from each trajectory are then merged to form ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2010