The Physical Exploration Challenge: Robots Learning to Discover, Actuate, and Explore Degreesof Freedom in the World

这个项目将为现实世界的机器人系统配备智能中最有趣的方面之一：学习的内在动力，即。即表现出能够最大限度地促进学习进程的行为的能力。具体来说，我们将设计机器人系统，通过推、拉和扭转看似有趣的部件来物理地探索它们的环境，目标是学习如何发现、驱动和探索世界中的自由度（DoF）。我们的方法采用了最先进的机器学习探索和主动学习研究，特别是优化预期信息增益策略的概念。然而，我们现实世界的探索场景提出了一些在现有研究中尚未解决的基本问题。三个开放的问题定义了本提案的主要研究领域：(1)~我们如何能够对运动结构形成一个可处理的信念，该信念代表了相关方面的相关不确定性，例如自由度的存在、它们的性质和它们的关系？考虑到智能体的感知和行为受到不确定性的影响，并且从经验中不断改进，哪些方法可以为这些表示导出信息增益驱动的探索策略？(2)~哪些感知方法对发现和识别自由度、形成关于有希望的交互点的假设以及将场景分割为刚体假设有用？(3)~我们如何参数化和优化运动原语，以便在探索的背景下成功地交互和操纵自由度？提出的研究解决了机器学习和机器人交叉领域的一个基本挑战。如果成功，我们相信这将在机器人系统的行为方式、自主学习方式、获取知识的方式，以及最终与人类互动的方式，以及在工业和私营部门的广泛应用中发挥作用方面产生过渡性的变化。

This project will equip real-world robotic systems with one of themost interesting aspects of intelligence: an internal drive to learn,i.e., the ability to exhibit behavior that maximizes learning progresstowards an objective. Concretely, we will design robotic systems thatphysically explore their environment by pushing, pulling, and twistingseemingly interesting parts, with the goal of learning how todiscover, actuate, and explore degrees of freedom (DoF) in the world.Our approach is informed by state-of-the-art Machine Learning researchon exploration and active learning, especially the concept ofoptimizing policies for expected information gain. Our real-worldexploration scenario, however, raises fundamental issues that have notpreviously been addressed in existing research. Three open questions define the major research areas of this proposal:(1)~How can we formulate a tractable belief over kinematic structuresthat represents the relevant uncertainties over relevant aspects, suchas the existence of DoF, their properties, and their relationships?What methods can derive information-gain driven exploration strategiesfor these representations, given that perception and actions of theagent are subject to uncertainty and are continuously improved fromexperience? (2)~What perception methods are useful for discoveringand identifying DoF, for forming hypotheses about promisinginteraction points, and for segmentating the scene into rigid bodyhypotheses? (3)~How can we parameterize and optimize motion primitivesfor the objective of successful interaction and manipulation ofdegrees of freedom in the context of exploration?The proposed research addresses a fundamental challenge in theintersection of machine learning and robotics. If successful, webelieve that this will make a transitional change in the way roboticsystems behave, in their autonomy of learning, in the way they groundacquired knowledge, and eventually also in the way they will interactwith humans and play their part in a wide range of applications inindustry and the private sector.