Achieving human-level performance on real world tasks is a north star for the robotics community. We present the first learned robot agent that reaches amateur humanlevel performance in competitive table tennis. Table tennis is a physically demanding sport that takes humans years to master. We contribute (1) a hierarchical and modular policy architecture consisting of (i) low level controllers with their skill descriptors that model their capabilities and (ii) a high level controller that chooses the low level skills, (2) techniques for enabling zero-shot sim-to-real and curriculum building, including an iterative approach (train in sim, deploy in real), and (3) real time adaptation to unseen opponents. Policy performance was assessed through 29 robot vs. human matches of which the robot won 45 % (13/29). All humans were unseen players and their skill level varied from beginner to tournament level. Whilst the robot lost all matches vs. the most advanced players it won 100 % matches vs. beginners and 55 % matches vs. intermediate players, demonstrating solidly amateur humanlevel performance. Videos of the matches can be viewed here1.See sites https://google.com/view/competitive-robot-table-tennis.

Assembly of multi-part physical structures is both a valuable end product for autonomous robotics, as well as a valuable diagnostic task for open-ended training of embodied intelligent agents. We introduce a naturalistic physics-based environment with a set of connectable magnet blocks inspired by children’s toy kits. The objective is to assemble blocks into a succession of target blueprints. Despite the simplicity of this objective, the compositional nature of building diverse blueprints from a set of blocks leads to an explosion of complexity in structures that agents encounter. Furthermore, assembly stresses agents’ multi-step planning, physical reasoning, and bimanual coordination. We find that the combination of large-scale reinforcement learning and graph-based policies – surprisingly without any additional complexity – is an effective recipe for training agents that not only generalize to complex unseen blueprints in a zero-shot manner, but even operate in a reset-free setting without being trained to do so. Through extensive experiments, we highlight the importance of large-scale training, structured representations, contributions of multi-task vs. single-task learning, as well as the effects of curriculums, and discuss qualitative behaviors of trained agents. Our accompanying project webpage can be found at: https://sites.google.com/view/learning-direct-assembly/home

This research investigated the psychological stressors during the COVID-19 pandemic and the effects of essential oil odor exposure. In Japan, a stay-at-home restriction order was implemented in May 2020. We sent essential oils to the homes of 30 participants. The participants received emails 5 times a day for 6 days and reported how they felt before and after the essential oil odor exposure. They also reported their circumstances and intentions. Results showed that the vitality and stability levels increased after essential oil odor exposure. Besides, four psychological stress structures were obtained. Some of the participants felt conflicted about balancing housework, childcare, and work. They were the most stressed, and their vitality and stability levels increased considerably.

Negotiation between robots is often needed in wireless ad-hoc robot network. In dynamic coverage problem, wireless ad-hoc robots move in the field negotiating with other robots frequently. Robots are tied with P2P network and determine their actions autonomously by negotiations with other robots. The topic of this paper is a negotiation for multiagent system (MAS) in the problem of robots activity in dynamic coverage, which is the problem of robots to cover all the areas of a free space by their sensors in the shortest possible time. This paper introduce a novel algorithm for robots' negotiation for MAS in the dynamic coverage problem by using PID-filter. This algorithm reduced useless motion of robots and decreased the time concerning the convergence of a robot's action for dynamic coverage problem. The claim made in this paper for new negotiation algorithm for MAS are supported by comprehensive experimental results and discusses future research directions