AI Technology & Industry Review
A research team presents a novel language-conditioned robot manipulation framework called Gen2Act, which achieves generalization to unseen tasks using publicly available web data, eliminating the need to collect specific robot data for every task.
In a new paper Achieving Human Level Competitive Robot Table Tennis, a Google DeepMind research team introduces the first robot agent that attains amateur human-level performance in competitive table tennis.
In a new paper PianoMime: Learning a Generalist, Dexterous Piano Player from Internet Demonstrations, a research team introduces PianoMime, a framework for training a robot to play the piano using internet-sourced demonstrations.
A research team from University of California, Berkeley presents a causal transformer model trained via autoregressive prediction of sensorimotor trajectories, culminating in the remarkable feat of enabling a full-sized humanoid to navigate the streets of San Francisco in a zero-shot manner.
In a new paper Behavior Generation with Latent Actions, a research team introduces the Vector-Quantized Behavior Transformer (VQ-BeT), an innovative model offers a solution for behavior generation, addressing multimodal action prediction, conditional generation, and partial observations.
DeepMind, in collaboration with 33 academic laboratories heralds the arrival of RT-1-X, a novel robotics transformer (RT) model that evolves from RT-1. RT-1-X is meticulously trained on the novel Open X-Embodiment dataset constructed by the researchers and showcases a remarkable 50% improvement in success rates compared to task-specified models.
A research team from Carnegie Mellon University, Google Brain and UC Berkeley proposes a robust predictable control (RPC) method for learning reinforcement learning policies that use fewer bits of information. This simple and theoretically-justified algorithm achieves much tighter compression, is more robust, and generalizes better than prior methods, achieving up to 5× higher rewards than a standard information bottleneck.
A research team from ETH Zurich combines continual learning and self-supervision to propose a novel robot system that enables online life-long self-supervised learning of semantic scene understanding.
According to official statistics released by China’s Ministry of Industry and Information Technology (MIIT), from January to October 2020, the nationwide industrial robots production grew to 183,447 units
Intel Labs researchers have proposed a novel method for building a robot called “OpenBot” on just a US$50 budget.
Cruciform Thin-film Microrobot (CTM) could realize double-modal motion and environmental adaptive functions through a programmable external magnetic field.