AI robot

We, Robot is Tesla's page showcasing its vision for self-driving technology and robotics. It underscores Tesla's commitment to creating a sustainable future that improves transportation efficiency, affordability and safety. This page introduces Tesla's fully self-driving technology (supervision) and potential applications for future self-driving cars and robots, such as Robotaxi, Robovan, and Tesla Bot. These technologies aim to increase the convenience of daily life through automation while reducing traffic accidents and lowering transportation costs.

HumanPlus is a research project aimed at training humanoid robots to achieve autonomous skill learning by imitating human movements. This project trains low-level policies through simulated reinforcement learning and applies these policies to the real world to achieve real-time tracking of human body and hand movements. Through shadow simulation technology, operators can remotely operate the robot to collect whole-body data for learning different tasks. In addition, through behavioral cloning technology, robots can imitate human skills and complete various tasks.

NVIDIA Project GR00T is a universal foundation model that changes how humanoid robots learn in simulation and the real world. By training in NVIDIA GPU-accelerated simulations, GR00T enables humanoid robots to learn from a small number of human demonstrations through imitation learning and NVIDIA Isaac Lab reinforcement learning, and generate robot actions from video data. The GR00T model accepts multimodal instructions and past interactions as input, and outputs the actions that the robot needs to perform.

The Universal Manipulator Interface (UMI) is a data collection and policy learning framework that allows direct transfer of skills from live human demonstrations to deployable robot policies. UMI uses a handheld gripper combined with careful interface design to enable portable, low-cost, and information-rich data collection for challenging bimanual and dynamic manipulation demonstrations. To facilitate deployable policy learning, UMI incorporates a carefully designed policy interface with inference-time delayed matching and relative trajectory action representation. The resulting learning strategy is hardware-independent and can be deployed on multiple robotic platforms. Equipped with these capabilities, the UMI framework unlocks new robotic manipulation capabilities by simply changing the training data for each task, allowing for generalized dynamic, bimanual, precise and long-term behaviors, thus enabling zero adjustments. We demonstrate the generality and effectiveness of UMI through comprehensive real-world experiments, in which a UMI policy trained solely using a variety of human demonstrations achieves generalization with zero adjustments when faced with new environments and objects.

OK-Robot is an open modular framework that enables zero-sample item handling tasks based on language instructions in any home environment. The framework adopts a modular design, using 3D VoxelMap for open vocabulary navigation, AnyGrasp and LangSam for open vocabulary grabbing, and placement primitives for item placement. The framework does not require pre-training and can achieve zero-shot generalization of language instructions.

Alter3 is an anthropomorphic robot based on GPT-4, which can generate various human actions through natural language instructions and achieve zero-shot learning. It has 43 joint degrees of freedom and can move freely, while combining facial expression recognition and generation to achieve complex interactions. Users only need to provide language instructions, and Alter3 can independently adjust the code and generate corresponding movements without manual iterative adjustments. Movements can also be optimized through verbal feedback to form motor memory. This system that combines language understanding and body movement greatly enhances the potential of human-computer interaction.