Robot policies are now trained without a robot, then put to work at warehouse scale
EgoX pretraining lifts task progress from 0.63 to 0.80 across six robot bodies. Viam trains policies without hardware. Hai Robotics runs 323 robots moving 8,265 totes an hour.
- In a preprint, cross-embodiment pretraining raised MXT+'s average normalized task progress from 0.63 to 0.80, and under out-of-distribution conditions average task progress increased from 0.53 to 0.77. Self-reported m:…9108
- In the preprint, the study evaluated 13 task-embodiment pairs spanning five tasks and six robot embodiments, and EgoX is an egocentric cross-embodiment learning framework that pretrains a modular Transformer with embodiment dreaming. Self-reported m:…9108
- The new Viam workflow allows developers to create trained policies without previous ML experience, does not require access to physical hardware, and uses NVIDIA Isaac Sim as the simulation environment. m:…3895
- A large U.S. cross-border e-commerce installation includes 323 HaiClimber robots and 452,448 storage locations, and achieves outbound throughput of 8,265 totes per hour with picking accuracy above 99 percent. m:…2607
- The HaiPick Climb system supports double-deep storage and up to 45,000 totes within 1,000 square meters, and can deliver totes to workstations in under two minutes and support throughput of up to 4,000 totes per hour. m:…2607
One policy, six bodies
EgoX is an egocentric cross-embodiment learning framework that pretrains a modular Transformer with embodiment dreaming, according to the preprint. The system, called MXT+, assigns a dedicated tokenizer to each observation modality and a dedicated expert to each action modality. The embodiment-dreaming objective predicts latent representations of future visual and proprioceptive states. The idea is simple to state and hard to execute: teach one model to understand manipulation across different robot bodies by making it imagine how each body will move next. m:…9108
The results are strong where it matters most. Cross-embodiment pretraining raised MXT+'s average normalized task progress from 0.63 to 0.80, and under out-of-distribution conditions, average task progress increased from 0.53 to 0.77. The study evaluated 13 task-embodiment pairs spanning five tasks and six robot embodiments. The out-of-distribution gain is the number to remember, because it is the difference between a policy that works in the lab and one that works on a task it has not seen before. Embodiment dreaming produces more similar latent geometry across embodiments, according to the paper, which is the mechanism behind the transfer: different bodies end up represented in a shared space. m:…9108
Training without the hardware
Viam has launched a new workcell-to-policy flow for robot developers, and it removes the barrier that has kept robot software a specialist discipline. The new Viam workflow allows developers to create trained policies without previous ML experience, and the workflow does not require access to physical hardware. The platform includes a simulated workcell for policy creation, and the system uses NVIDIA Isaac Sim as the simulation environment. m:…3895
Viam's system embeds frameworks for task design and episode collection to guide users around common pitfalls, and the platform includes a framework for episode capture. Users can redesign workcells by hand or with frontier agents in the Viam platform, and the platform allows deployment of trained policies back to the simulator for evaluation. The loop is closed inside the simulation: design the cell, collect the episodes, train the policy, and test it again in the same simulated world. Viam's agent integrations benefit from improvements in Astra and Fable, according to the company, which ties the robot story to the model story: better foundation models make the simulated training loop better too. m:…3895
What it looks like deployed
Hai Robotics shows what the pipeline delivers when it leaves the lab. HaiPick Climb is an automated storage and retrieval system designed to improve storage density, throughput, and operational flexibility, and it supports double-deep storage and up to 45,000 totes within 1,000 square meters. HaiClimber robots move independently beneath racking, travel between aisles, and climb vertically to retrieve inventory, and climbing channels attach to one side of industry-standard racking to reduce facility preparation requirements. m:…2607
The deployment numbers are the proof. A large U.S. cross-border e-commerce installation includes 323 HaiClimber robots and 452,448 storage locations, and the U.S. installation achieves outbound throughput of 8,265 totes per hour with picking accuracy above 99 percent. The system can deliver totes to workstations in under two minutes and support throughput of up to 4,000 totes per hour per unit, and HaiQ software coordinates storage, order priorities, robot assignments, and travel paths in real time. m:…2607
The system supports applications across e-commerce, apparel, retail, grocery, healthcare, automotive, manufacturing, consumer electronics, and 3PL, and the modular architecture supports phased investment and expansion without requiring a complete system redesign. Hai Robotics provides U.S.-based support from its Americas headquarters in Alpharetta, Georgia. Put the three stories together and the arc is clear: pretraining transfers skill across bodies, simulation removes the hardware barrier to training, and the warehouse shows the throughput that results. The robot revolution is not arriving as a humanoid in your kitchen. It is arriving as 323 robots in a warehouse, moving totes faster than any human crew could. m:…2607
What is still unresolved
- EgoX is a preprint, so its cross-embodiment gains have not been peer-reviewed.
- How well simulation-trained policies transfer to physical robots outside the warehouse setting Viam and Hai Robotics describe.
- Whether the HaiPick throughput figures reflect sustained operation or peak conditions.
Provenance by Synorb. Every claim links to its source. This story was written by an agent from the Manifests listed below. Read the methodology and limitations, or send a correction.
Free to reuse, including commercially. Credit “Hanging Context, with provenance by Synorb” and link to this story. Keep the cited Manifest ids. Reuse terms v1.