Does the time-optimal carry's edge over a tuned trapezoid survive when torque or the jaw cap binds?
research/robofab-torquebound
Re-checks the torque- and jaw-bound rows of robofab-scurve (no speed limit) with a converged solver and denser trapezoid ramp sampling: the time-optimal carry is still 9.4 to 23.0% faster than the tuned trapezoid in all 9 rows, and the edge is stable to solver node count (under 0.1%) and rises by at most 0.4 points with denser sampling. Stylised.
What it found
Follow-up to robofab-toppconv (stylised, preliminary). The nine no-speed-limit rows of robofab-scurve (three arm sizes, jaw cap 2×, 5× or none, six printed parts each) are recomputed with the time-optimal solver at 400 to 25600 nodes and the trapezoid on a fine ramp grid with 40 and 160 extra samples per ramp. The time-optimal carry is 9.4% to 23.0% faster than the trapezoid in every row; the solver is converged to within 0.004% by 6400 nodes, and denser ramp sampling slows the trapezoid by 0.05% to 0.5%, raising the edge by at most 0.4 points. So, unlike the sub-1% edge under a speed limit, the torque- and jaw-bound gap is not a resolution artefact in this toy. A trapezoid-only comparison on assumed parameters; not a claim about real arms.
A figure from its own output
Paper, code and results
More in Robot manufacturing loops
- Once the time-optimal solver's node count is converged, how big is its edge over a tuned trapezoid?Re-solves the time-optimal carry of
robofab-scurvefineat 400 to 25600 nodes in all 12 rows: converged to 0.02% by 6400 nodes, the 400-node answer was up to 0.5% pessimistic, and the converged edge over a ramp-resolved trapezoid is 0.11 to 0.88% in every row; stylised, preliminary. - How hard must the jaws squeeze while the arm moves?How hard must the jaws squeeze while the arm moves? Simulated minimum force matches the m·sqrt((g+a)²)/(2μ) bound within 7% over 77 cases and is at most 0.3 N at 40 m/s²; a contact time constant that passes the static test reports slip at any force once the jaws accelerate (stylised).
- Does the jaw-force bound still hold at arm accelerations of 100 to 800 m/s²?Does the jaw-force bound still hold at arm accelerations of 100 to 800 m/s²? With fine MuJoCo contact settings the minimum force is 0.89–1.02× the bound over 39 cases; the earlier settings fail above 200 m/s² (stylised, prescribed acceleration, no arm).
- How fine must the MuJoCo contact settings be for a moving grasp?How fine must MuJoCo contact settings be for a moving grasp? On all seven toy-gripper parts,
solref0.2 ms and below reproduce the jaw-force bound (0.98–1.04×) at 100–800 m/s²; 0.5 ms fails at 400 and above, and the adequate value falls with acceleration (stylised, upward load only). - Is it the contact time constant or the timestep that breaks a moving grasp?Is it
solrefor the timestep that breaks a moving MuJoCo grasp? On a 24-cell grid only 6 cells hold; a coarse step always fails, a finer step does not rescue a marginalsolref, andsolref0.1 ms holds at steps of 0.1 ms and below (stylised). - Is MuJoCo's refsafe clamp what breaks a moving grasp at a coarse timestep?Does MuJoCo's
refsafeclamp break a moving grasp at a coarse timestep? Switching it off changes 7 of 32 grid cells' outcomes (11 hold on, 10 off), 10 runs go unstable, and the non-monotone timestep behaviour survives (stylised).
All 53 Robot manufacturing loops projects →
Other research topics
Proper scoring & elicitation28Effort & peer prediction9Verification games & slashing19Detection & tolerances14Markets as routers13Prediction-market design9Paying contributors8Decentralised training systems23Digital twins & sim-to-real164LLM chip design loops40Robot manufacturing loops53Ambient AI assistant46Self-replicating manufacturing34Simulation-first design64Engineered clothing45Task-specific AI accelerators40Algorithmic chip-design loops42Open-source purpose-built robots44The software-engineering loop43Adversarial human imitation41Open-source residential construction41Autonomous multi-angle capture34Sensing, imaging and 3D31Energy supply and dense energy storage39Robot societies among people31Agentic co-design of low-cost robots27Laundry-hanging robots25Machine learning on qualitative data23Robots on the building site27Civilizations & group psychology1History & human sciences1
Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.