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Of the 39,800 seconds of actual driving with 13 models from three manufacturers, the track maintenance aid indicator shows only 47% of times when the perception of markings is degraded.
In simulation on a unit G1, the policies learned under terrestrial gravity still stand on the Moon, while those trained for the Moon collapse on Earth.
The manufacturer of electric cars turns to robotics with two miniature humanoids, first passed to the dropper on invitation.
Shown by Nvidia's boss at the GTC Berlin, these robots trained in simulation work, sit down and make saltos to the voice. Delivery promised before Christmas.
The Alpha Fault Injection Bench shows that the 20-point gap between an agent Llama 3 and a deterministic planner comes entirely from corrupt outputs, not reasoning.
Pre-recorded study on a vision-driven challenge: reconnecting recovery memory to a LoRA policy adds 34 points, distillation alone barely 1 point.
By alternating the lighting, the sensor alternates to capture an image through its membrane and a touch print, from the same point of view and without calibration between cameras.
This method combines a learned world model, charged with anticipating mobile obstacles, with a differentiable quadrirotor simulator, and converges faster than existing approaches in mobile door racing.
Researchers publish a 3D touch sensor and its entire software ecosystem; a CAD tool generates manufacturing files from a contour or text description.
A Transformer pre-entered only on synthetic data generated by program manages to predict the actions of real robots on tasks he has never seen.
A careful planner predicts where the robot will lay hands and feet, a controller translates this plan into whole body movement: a single policy, without a deployment path.
The young shoot, founded in 2024, turns real data into controllable 3D environments. Rivian wants to generate the rare cases that his driving aid and future robotaxis need.
The Swiss start-up, founded by an ex-Physical Intelligence, focuses on tactile perception via actuators rather than on vision alone to make manipulating objects reliable.
This system continuously updates a robot policy based on the interventions of an operator and his own tests, without criticism learned or reward model.
A single control policy, trained in several stages and limited to embedded sensors, allows precise point-to-point jumps in simulation, and a first vertical jump was made on a real robot.
A four-part assessment shows that state-of-the-art large models understand instructions very well, but remain significantly less reliable when it comes to physically handling objects.
The start-up of two former SpaceX aircraft, which automates existing aircraft from taxiing to landing, is waiting for a first FAA certification in 18 to 24 months.
MIT professor Pulkit Agrawal's startup is valued at $1.45 billion after a Series A round led by a16z and F-Prime.
Without dedicated training, OpenAI's model interprets the provided touch images as simple photos and achieves 93.8% success on simulated tasks where specialized models were much lower.
The family office of the owner of the New York Mets, Certares and Knighthead is investing in Oro, which already maintains 45,000 vehicles for ride-hailing companies and is positioning itself in the operation of autonomous taxis.