Does pick order matter for a robotic waste-sorting cell once items differ in value and arm travel?

research/infra-sortvalue

Follow-up to infra-sorting: with TACO's class mix, assumed item values (drink can = 4 plastic bottles) and 0.5 to 1.5 s of arm travel, picking the highest-value item first beats FIFO by only 0.1% at 1 item/m, 2.0% at 6/m and 4.8% at 10/m, and value-per-second picking matches it; belt speed and pick time matter far more than pick order.

6 tests

What it found

infra-sorting found that FIFO, LIFO and largest-first tie because every item had the same value, pick time and window. This project gives items a value by TACO category (assumed: a drink can is worth 4 plastic bottles, swept 1 to 16; glass 0.15) and a pick time of 0.5 to 1.5 s from lateral arm travel, and compares FIFO, highest-value-first and value-per-second dispatch on identical simulated arrival streams (40 runs, paired, 95 % intervals). Value-first beats FIFO by 0.1 % at 1 item/m, 0.6 % at 3, 2.0 % at 6 and 4.8 % at 10; the gain rises with can value (+1.1 % to +2.6 %), peaks for a mid-speed arm (0.6 m/s) and is near zero for a very slow or very fast one, and grows to +4.2 % when each contaminant costs two bottles. The two value-aware rules tie. Category values, arm speed, detector, grasp and reach are assumed (only can value and arm speed swept), the detector is not trained, and only greedy rules are tried, so this is no upper bound on dispatch gain; a real cell is not measured.

A figure from its own output

Figure produced by research/infra-sortvalue: load — infra-sortvalue/experiments/load.svg, written by the project's own experiment code.

Paper, code and results

More in Robot manufacturing loops

Which assembly orders survive a better checker?Assembly-order planning for a printed gearbox: exact subset DP, collision-only admits 10.6% of orders but support checking leaves 0.077%, the collision-only optimum is infeasible, greedy disassembly needs 4 direction changes vs 1 optimal. Stylised.
Does a quasi-static support rule agree with a physics engine?MuJoCo settle tests of all 2,048 partial gearbox assemblies vs the quasi-static support rule: 95.6% agreement, 81 mutual-support false accepts and 10 bore-capture false rejects, valid-order count unchanged at 30,760. Stylised.
Does an insertion policy tuned in one simulator survive a better one?Peg-in-hole search policies tuned by CEM in MuJoCo at three fidelity levels or with domain randomisation: tuned on the simplest level 0.91 success there but 0.53 / 0.26 on a stiffer-contact / held-out level; training on the stiffer level or with randomisation gives 0.43–0.46 held-out and cannot be ranked with 5 seeds. Stylised, sim-to-sim.
How much of the gap between “the twin passes” and “it fails for real” does each twin fidelity close?Session-written pick-and-place programs checked in a kinematic SCARA twin at three fidelities against a held-out reference: of 69 reference-failing injected-bug mutants the levels reject 38, 46 and 60 with no false alarms; misses are unmodelled effects (stylised).
Which resource limits a small robot-printer-inspection cell, and does the busiest one tell you what to upgrade?Discrete-event twin of an arm, printers and an inspection station: printers have the highest utilisation at every size, yet at 6 printers an extra arm saves 10.9 min vs 2.3 for an extra inspection station; dispatch rules trade makespan for flow time; a mean-value twin is optimistic. Stylised, illustrative parameters.
Where is the bottleneck of an arm, printer and inspection cell?Discrete-event twin of an arm, 3D printers and an inspection station: a utilisation bound names the bottleneck (printers to 10, the arm from 12, ~12.7 good parts/h) and the simulation sits 10–11% below it near the switch; a 25% faster arm lifts 11.34 to 12.68 parts/h at 12 printers, a second inspector nothing (stylised).

All 53 Robot manufacturing loops projects →

Other research topics

Proper scoring & elicitation28 Effort & peer prediction9 Verification games & slashing19 Detection & tolerances14 Markets as routers13 Prediction-market design9 Paying contributors8 Decentralised training systems23 Digital twins & sim-to-real164 LLM chip design loops40 Robot manufacturing loops53 Ambient AI assistant46 Self-replicating manufacturing34 Simulation-first design64 Engineered clothing45 Task-specific AI accelerators40 Algorithmic chip-design loops42 Open-source purpose-built robots44 The software-engineering loop43 Adversarial human imitation41 Open-source residential construction41 Autonomous multi-angle capture34 Sensing, imaging and 3D31 Energy supply and dense energy storage39 Robot societies among people31 Agentic co-design of low-cost robots27 Laundry-hanging robots25 Machine learning on qualitative data23 Robots on the building site27 Civilizations & group psychology1 History & 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.