With the increasing presence of robots in our every-day environments, improving their social skills is of utmost importance. Nonetheless, social robotics still faces many challenges. One bottleneck is that robotic behaviors need to be often adapted as social norms depend strongly on the environment. For example, a robot should navigate more carefully around patients in a hospital compared to workers in an office. In this work, we investigate meta-reinforcement learning (meta-RL) as a potential solution. Here, robot behaviors are learned via reinforcement learning where a reward function needs to be chosen so that the robot learns an appropriate behavior for a given environment. We propose to use a variational meta-RL procedure that quickly adapts the robots' behavior to new reward functions. As a result, given a new environment different reward functions can be quickly evaluated and an appropriate one selected. The procedure learns a vectorized representation for reward functions and a meta-policy that can be conditioned on such a representation. Given observations from a new reward function, the procedure identifies its representation and conditions the meta-policy to it. While investigating the procedures' capabilities, we realized that it suffers from posterior collapse where only a subset of the dimensions in the representation encode useful information resulting in a reduced performance. Our second contribution, a radial basis function (RBF) layer, partially mitigates this negative effect. The RBF layer lifts the representation to a higher dimensional space, which is more easily exploitable for the meta-policy. We demonstrate the interest of the RBF layer and the usage of meta-RL for social robotics on four robotic simulation tasks.
A variational approach to geometric mechanics for undulating robotic locomotion
University of Notre Dame（圣母大学）；California Institute of Technology（加州理工学院）；Dublin City University（都柏林城市大学）；EPFL (Swiss Federal Institute of Technology, Lausanne)（洛桑联邦理工学院（EPFL））
PinPoint: Monocular Needle Pose Estimation for Robotic Suturing via Stein Variational Newton and Geometric Residuals
Department of Mechanical Engineering, Vanderbilt University（范德比尔特大学机械工程系）；Department of Electrical and Computer Engineering and Department of Computer Science, Vanderbilt University（范德比尔特大学电气与计算机工程系和计算机科学系）；The Robotics Center and the Kahlert School of Computing, University of Utah（犹他大学机器人中心和Kahlert计算学院）
Meta-Learning for Fast Adaptation in Intent Inferral on a Robotic Hand Orthosis for Stroke
Department of Mechanical Engineering, Columbia University in the City of New York（机械工程系，哥伦比亚大学（纽约市））；Department of Computer Science, Columbia University in the City of New York（计算机科学系，哥伦比亚大学（纽约市））；Department of Rehabilitation and Regenerative Medicine, Columbia University Irving Medical Center（康复与再生医学系，哥伦比亚大学伊万斯医学中心）
MetaTune: Adjoint-based Meta-tuning via Robotic Differentiable Dynamics
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；School of Electrical and Computer Engineering, National University of Singapore（新加坡国立大学电气与计算机工程系）；The Shenzhen Huazhong University of Science and Technology Research Institute（华中科技大学深圳研究院）
Bayesian Optimization for Developmental Robotics with Meta-Learning by Parameters Bounds Reduction
LIRIS, CNRS UMR 5205 Ecole Centrale de Lyon（里昂中央理工学院 LIRIS 实验室（法国国家科学研究中心 UMR 5205））
在机器人技术中，方法和软件通常需要优化超参数，以便有效地执行特定任务，例如从不同对象的同质堆中进行工业装箱。我们提出了一个发展框架的基础上长期记忆和推理模块（贝叶斯优化，视觉相似性和参数边界减少），允许机器人使用元学习机制，提高这种连续和约束参数优化的效率。新的优化被视为机器人的学习，可以利用过去的经验（存储在情节和过程存储器中），以通过使用从具有新任务的机器人实现的最佳优化计算的减少的参数边界来缩小搜索空间（例如，基于存储在语义记忆中的对象的视觉相似性，从相似对象的同质堆中进行装箱）。作为例子，我们已经面对系统的9个连续的超参数的约束优化的专业软件（Kamido）在工业机器人手臂装箱任务，每次都需要正确地处理新的对象的一个步骤。我们使用模拟器为8个不同的对象创建装箱任务（7个在模拟中，一个在真实设置中，没有和有来自其他类似对象的经验的元学习）尽管优化预算很小，但仍实现了良好的结果，当使用元学习时，性能更好（总体成功率为84.3% vs 78.9%，每次优化的小预算为30次迭代）（p值=0.036）。
Boosting Action-Information via a Variational Bottleneck on Unlabelled Robot Videos
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）
Hybrid Robotic Meta-gripper for Tomato Harvesting: Analysis of Auxetic Structures with Lattice Orientation Variations
Tohoku University, Japan（东京东京大学）；Delft University of Technology (TU Delft), the Netherlands（代尔夫特理工大学）；Indian Institute of Technology Kanpur (IIT Kanpur), India（印度理工学院坎浦尔分校）
End-to-end example-based sim-to-real RL policy transfer based on neural stylisation with application to robotic cutting
Extreme Robotics Lab, School of Metallurgy and Materials, University of Birmingham（极端机器人实验室、冶金与材料学院、伯明翰大学）；The Faraday Institution, Quad One, Harwell Science and Innovation Campus（法拉第机构、Quad One、哈威尔科学与创新校园）；School of Computer Science and Digital Technologies, Aston University（计算机科学与数字技术学院、阿斯顿大学）
Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis
CMU（卡内基梅隆大学）；Bosch Center for AI（博世人工智能中心）；MIT（麻省理工学院）；Field AI（菲尔德人工智能公司）；Georgia Tech（佐治亚理工学院）；FAIR at Meta（元公司人工智能研究院）；UC San Diego（加利福尼亚大学圣迭戈分校）；SAIR Lab（空间人工智能与机器人实验室）；Google DeepMind（谷歌DeepMind）
