# Nociception as a Control Primitive: Afferent Channels and Nociceptive Memory for Agents Deployed in One Body

arXiv: https://arxiv.org/abs/2609.34840 · HTML: https://mlsanity.com/paper/2609.34840/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 83 references that are in the map.
- First posted on arXiv 28 Sep 2026.

## Builds on (most cited first, 29 of 29)

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- [RL2: Fast Reinforcement Learning via Slow Reinforcement Learning](https://mlsanity.com/paper/1611.02779/) (2016)
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- [Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables](https://mlsanity.com/paper/1903.08254/) (2019)
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- [Towards Continual Reinforcement Learning: A Review and Perspectives](https://mlsanity.com/paper/2012.13490/) (2020)
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- [Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach](https://mlsanity.com/paper/1506.02188/) (2015)
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- [Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs](https://mlsanity.com/paper/2106.02684/) (2021)
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- [Approximate information state for approximate planning and reinforcement learning in partially observed systems](https://mlsanity.com/paper/2010.08843/) (2020)
- [Conservative Bandits](https://mlsanity.com/paper/1602.04282/) (2016)
- [Conservative Contextual Linear Bandits](https://mlsanity.com/paper/1611.06426/) (2016)
- [Conservative Agency via Attainable Utility Preservation](https://mlsanity.com/paper/1902.09725/) (2019)
- [DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning](https://mlsanity.com/paper/2112.00885/) (2021)
- [Managing engineering systems with large state and action spaces through deep reinforcement learning](https://mlsanity.com/paper/1811.02052/) (2018)
- [You Only Live Once: Single-Life Reinforcement Learning](https://mlsanity.com/paper/2210.08863/) (2022)
- [There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning](https://mlsanity.com/paper/2106.04480/) (2021)

## Abstract (arXiv)

An agent deployed in a single body cannot learn how fast that body wears, because every trial that would reveal its wear resistance wears the body it would protect. We study this epoch-one setting, in which the parameters of a fixed-weight policy are set before the body is drawn and never updated in life. The agent carries a load-gated nociceptive channel and a memory that retains what was felt. We prove that felt cost moves the allocation to the best-paid work not yet felt rather than the gentlest, that an agent without retention never sees the felt-cost constraint bind, and that the channel pays only where the threat is individually unpredictable, cheap to avoid and expensive to ignore. We measure per body, setting the agent with channel and memory against the same individual without them, where neither carries a schedule learned across lives. On 2,000 simulated floor-layer knees, with wear anchored to published loss rates, feeling, retaining and substituting extends the working life from age 55.2 to 59.6 and raises career output from 33.7 to 36.1. 69.3% of bodies gain and none lose. A body that feels but retains nothing past the day gains one of the +4.4 years, and retention carries the rest. A population-trained agent gains +0.65 years from the same channel at -0.54 output. The difference is what a species prior already supplies, and a single body has none. The two are related by an identity, the ablation mean reporting (1-chi) of the per-body value with chi the share a blind schedule already captures, so we report both. Where the regime map predicts value, a care robot sextuples its certified service life and a field-anchored fleet writes off 0.15 of its machines instead of 0.55. Where it predicts none, a rover gains little over blind caution, so the map holds in both directions.