FACTR 2: torque sensing from 10 minutes of free motion — no force sensor, but the current sensor it does need is one we don’t log

Read 2026-08-09/10 (lit slice lit-radar-0820, priority 2). Paper: 2606.12406 — “FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning” (Steven Oh, Jason Jingzhou Liu, Tony Tao, Philip Han, Kenneth Shaw, Satoshi Funabashi, Ruslan Salakhutdinov, Deepak Pathak; CMU; arXiv cs.RO, submitted 2026-06-10; arXiv nonexclusive distribution license; project page jasonjzliu.com/factr2 verified live, code “Coming Soon” — not released as of 2026-08-09; predecessor FACTR verified live with released training/teleop/hardware repos).

The paper in plain words. Feeling what you touch is most of what makes contact-rich manipulation work, and most robot arms can’t do it — real force sensors are expensive, so cheap arms ship without them. This paper’s trick: every motor already leaks a force signal through its electrical current, but that raw signal is polluted by everything else the motor is doing — fighting gravity, fighting its own friction, accelerating the arm. So they drive the arm around in free space for ten minutes, touching nothing, and train a small recurrent network (one minute of training) to predict what the motor torque should be when no contact is happening. At run time, whatever the measured torque shows beyond that prediction must be the outside world pushing back. On a $30, 000 F r anka t hi sres i d u a lt r a c k s t h e f a c t ory t or q u ese n sorsc l ose l ye n o ug h t o d r i v e f orce - f ee d ba c k t e l eo p er a t i o n t ha t 20 s t u d y p a r t i c i p an t sr a t e d a se a sy a s t h ere a l - se n sor v ers i o n . T h e n t h eyc l ose t h e l oo p in t o l e a r nin g : l ab e l e a c h d e m o n s t r a t i o n f r am e a s f ree - s p a ce, ab o u t - t o - t o u c h, or in - co n t a c t u s in g t h ees t ima t e d t or q u e, an df ee d t h e b e ha v i or - c l o nin g s am pl er f i v e t im es m oreo f t h e ab o u t - t o - t o u c h f r am es . T ha t re - w e i g h t in g — pl u s g i v in g t h e p o l i cy t h e t or q u es i g na l a s anin p u t — b e a t s p r i or f orce - a w a re p o l i c i es b yo v er 17 p ro g resso n f i v e l o n g - h or i zo na sse mb l y t a s k s . T h ec a t c h f or u s : " n o f orcese n sor " d oes n o t m e an " n ose n sor ." T h e m e t h o d^{'} sr a w ma t er ia l i s m o t orc u rre n t a t 100 Hz, an d t h ec h e a p es t a r mi t e v er t o u c h es i s a$ 2,500 AgileX Piper — our SO-101’s hobby servos and our positions-only community corpus are both below its floor. What survives the descent is the idea: contact phases are learnable from motor-side residuals, and up-weighting the moments just before contact is where the policy gains live.

What it contributes

NEXT (Neural External Torque Estimation): train f_θ(x) → τ̂_free on ~10 minutes of contact-free motion, where x is a 50-step history of joint position, joint velocity, and the commanded-minus-measured position difference Δq_d; at run time τ̂_ext = τ_m − τ̂_free, with measured motor torque obtained as current times torque constant, τ_m = K·I_m. Architecture: 2-layer LSTM (hidden 128) + 2-layer MLP head (hidden 256), L2 regression, AdamW; 1 minute of training on an RTX 3090; runs at 100 Hz in deployment (568 Hz capable). The pitch is that the learned model absorbs what analytical models can’t: “nonlinear friction, stiction, backlash, hysteresis, temperature-dependent drive behavior, sensing noise, torque ripple, deadzones, and saturation.”
Force-feedback teleop without force sensors: NEXT’s estimate drives FACTR-style bilateral teleoperation on arms that lack dedicated sensing (Franka with sensors ignored; AgileX Piper which never had them).
FIRST (Force-Informed Re-Sampling Training): segment each demo into free-space / pre-contact / contact by hysteresis thresholding the L1 norm of τ̂_ext (pre-contact = the 1-second window before contact onset), then up-sample the contact-relevant phases during BC batch construction — sampling weights w_F : w_PC : w_C = 1:5:1 on most tasks, 1:3:3 for cap screwing. The policy itself is a flow-matching head (conditional velocity field) over DINOv3-Base image tokens plus MLP-projected proprioception and the estimated external torque — o_t = (images, q_t, τ̂_ext,t).

The experiments it actually ran

Torque-estimation accuracy (Franka, ground truth = the factory-calibrated external-torque estimate from built-in joint torque sensors; 5 min held-out data with a human applying forces): NEXT contact error 0.547±0.348 Nm vs FILIC 4.395±1.531 and a disturbance observer 1.471±0.761; free-space error 0.414±0.278 Nm, below the dedicated external sensor estimate’s 0.449±0.208. On the Piper, free-space error 0.018±0.012 Nm. Input ablation: (q) < (q, q̇) < (q, q̇, Δq_d) — the position-tracking-error feature “consistently outperforms,” i.e. it is the load-bearing input. History length swept 10/25/50, 50 selected.
Teleoperation user study: a wiping task on the Franka, 20 participants, five conditions (no feedback, disturbance observer, leader-follower position feedback, FACTR teleop with dedicated sensors, FACTR teleop with NEXT). NEXT-based feedback rated easier to use than the baselines on 1–5 ratings, applied joint torques comparable to the sensor-based condition; repeated on the Piper.
Policy learning: five long-horizon contact-rich tasks on a bimanual Piper rig — LEGO assembly, NIST belt assembly, NIST insertion, tool clean-up, cap screwing — 250 demos per task, 20 rollouts per task, metric = task progress (fraction of completed stages per rollout; e.g. LEGO has 6 stages). Baselines: base policy, base + torque input, FACTR, TA-VLA. FIRST is highest on all five tasks; the abstract’s claim is “outperforms prior force-aware policies by over 17% in task progress.” Exact per-arm averages live only in Figure 6 (my figure read: base ~0.55, base+torque ~0.60, FACTR ~0.63, TA-VLA ~0.65, FIRST ~0.81 — treat as approximate).
The phase ablation (Table 2, the number that matters for curation): up-sampling pre-contact frames averages 0.818 task progress vs 0.670 for contact-only up-sampling and 0.811 for both — the second before touch is where the useful gradient lives, not the contact plateau itself.
Stated limitations: absolute torque scale depends on the motor torque constant K (“if K is inaccurate, absolute scale requires calibration”), and NEXT is robot-specific — retrain per arm.

Corrections to our banked hook

“No force sensor” has a hidden clause: a current sensor is load-bearing. τ_m = K·I_m is an input, at both training and inference, at 100 Hz. “Commodity” here means a $2,500 AgileX Piper and a Franka — the paper never touches the hobby-servo class, and SO-100/SO-101 appear nowhere in it.
The “+17%” is not re-sampling alone. FIRST both feeds τ̂_ext to the policy as an observation and re-weights the sampler; the +17% is over prior force-aware baselines (FACTR, TA-VLA) that also consume force. The paper never ablates re-sampling without the torque input — so the corpus-curation transfer our hook proposed (sampling-only, no force at inference) is unvalidated even inside the paper. The clean re-sampling datum is Table 2’s 0.818 (pre-contact) vs 0.670 (contact-only), both arms torque-conditioned.
“~10 min of motion data” verified, but it is 10 minutes of instrumented motion — free-space trajectories with motor current logged, plus 1 minute of training. Motion alone (our corpus’s positions-only logs) is not enough.
No artifact yet: project-page code is “Coming Soon” (checked 2026-08-09); only FACTR 1’s repos are released. The hook’s “cheapest force recovery” framing priced in an implementation that does not currently exist in public.

What transfers to us and what doesn’t

Not NEXT itself, on the corpus. community_curated_v0 logs cameras + observation.state (joint positions) + action (commanded positions) at 30 fps. No motor currents, no torques — the residual τ_m − τ̂_free is uncomputable, full stop. Joint velocities aren’t logged either, though finite-differencing at 30 fps is serviceable.
But the load-bearing feature IS in every episode. The paper’s own input ablation crowns Δq_d — commanded minus measured position — and that is exactly action − observation.state, present in all ~52.5k corpus episodes. A NEXT-shaped variant needs no current: train the same tiny LSTM to predict free-motion tracking error from (q, q̇) history, and use the residual of actual vs predicted tracking error as a contact score. On position-controlled servos with high gear friction, raw |Δq_d| spikes on fast motion and gravity load too — which is precisely why the residual-vs-learned-free-model structure, the paper’s actual idea, is the part worth copying.
Rig-side (SO-101 servos): Feetech STS3215s report Present_Load — a PWM-duty proxy, not calibrated current — at bus rates well below the paper’s 100 Hz, with no trustworthy K and gearbox friction/backlash far beyond a Piper joint. Absolute Nm is out of reach (the paper’s own K-calibration limitation, squared); a relative contact detector from the same recipe (10 min free motion, predict the load signal, threshold the residual) is plausible and rig-day-sized, but unproven at this servo class by anyone, including this paper.
The curation idea transfers as a hypothesis, not a result. Phase-aware up-weighting with pre-contact >> contact is a measured sign at 250-demo single-task scale, force-conditioned, on one rig — three qualifiers away from “re-weight 229 h of community BC data by an estimated contact signal.” It earns a screen, not a corpus.

What it fed

Idea #9 (data levers) — a new candidate for the weighted-sampling slot, with a zero-GPU gate first. #9’s “judge-score-weighted sampling (never yet run)” now has a literature-backed sibling: phase-weighted sampling by estimated contact proximity, with FIRST’s 1:5:1 pre-contact weighting and the 0.818-vs-0.670 pre-contact>contact ordering as the design prior. Cheapest next action (VISTA-screen pattern, zero GPU): an offline segmentation screen over the corpus — per-episode, compute Δq_d = action − state, fit the free-motion tracking-error model (or start even cheaper: per-joint normalized |Δq_d| hysteresis), and validate the derived contact onsets against gripper-close commands as weak grasp labels on a spot-check sample. Falsification is cheap and loud: if phase shares are degenerate (contact ≈ everywhere or ≈ nowhere) or onsets don’t line up with grasp events, the lever dies before any training. Only a clean screen buys the paired 40k re-sampling arm — and that arm must carry the caveat that sampling-only (no force input) is unvalidated even in FACTR 2.
Idea #16 (rig benchmark, parked) — a rig-day note, not an action. When the owner’s better rig dataset gets collected, the 10-minute free-motion protocol is worth folding into the collection day: log Present_Load + positions during free motion, train the 1-minute LSTM, and check whether the residual spikes on contact. If it does, the rig gets a free contact channel for FIRST-style weighting of its own fine-tuning data — and a force signal our panels have never had. No arm justified now: execution on #16 is owner-parked, and the servo-class feasibility question is exactly what the 10-minute protocol answers for free later.