Robot learning papers concentrate on execution control, retention, and cheaper real-world validation

FineVLA targets a specific weakness in robot datasets: many trajectories say what task was completed, but leave out how it was done. The work adds human-verified execution instructions for arm choice, approach direction, contact region, final pose, and other action details across 47,159 selected trajectories. Training with a mix of fine-grained and raw labels gives the best reported results, with AlohaMix-OFT reaching 86.8% Easy and 82.5% Hard on RoboTwin. In real dual-arm manipulation, the same line of evidence reports 62.7/100 for a 1:1 fine-grained-to-raw mix, compared with 49.9 for raw-only training.

The continual VLA study makes forgetting measurable on hardware-style tasks. Plain sequential fine-tuning drops earlier skills hard: Stack Bowl falls from 100.0 to 15.0, Hang Cup from 97.5 to 25.0, and Press Button from 100.0 to 13.3. Experience replay changes the outcome when replay rate and action normalization are set carefully. With buffer ratio 0.2 and replay frequency 0.2, the final average score reaches 93.5, compared with 37.3 for sequential fine-tuning. The action-normalization result is a useful warning: per-task normalization collapses to a 23.7 average, suggesting deployment details can dominate the learning method.

HyperSim and SDPG both attack the cost of training visual robot policies. HyperSim uses reconstructed scenes, adversarial synthetic trajectories, and 35 real demonstrations for co-training. In few-shot tests, mixed sim-real data reaches 95% SR3 with pi0, above the 70% real-only baseline with the same number of real demonstrations. SDPG reduces the rendering and memory burden for visual reinforcement learning by estimating action-sequence gradients with random perturbations. Its reported memory use is about 10.2 to 10.5 GB across Visual MuJoCo tasks, while the PPO estimate is about 48 to 50 GB.

Figure reports a 200-hour autonomous package-sorting run by Figure 03 humanoids, with 249,560 packages sorted. The task is concrete: locate the barcode on a small package and place it face-down on a conveyor. The article also gives a human comparison from an earlier 10-hour run, where the human sorted 12,924 packages and the robot sorted 12,735. The claim is useful as an endurance marker, but the source does not provide an independent audit, error rate, uptime breakdown, or comparison with existing warehouse automation.