"Smarter AI won’t come from erasing the body; it will come from giving memory a pulse—so when the form changes, the feeling stays."

— Aditya Mohan, Founder, CEO & Philosopher-Scientist, Robometrics® Machines

We speak of “uploading” minds as if memory were a format and the body a removable drive. Yet anyone who has trembled at an old song knows that the flesh is not a container but a collaborator. Scars, posture, breath cadence, micro-tension in the hands—these are not metadata; they are the data. In humans, state-dependent memory couples recall to physiology: feel what you felt, then you can see what you saw. “A purely disembodied human emotion is a nonentity,” wrote William James, and Maurice Merleau-Ponty added, “The body is our general medium for having a world.” Eternal life, in this light, is not endless duration of thought; it is continuity of felt experience across time. When we move the mind from body to body, what degrades is not mere performance but the spirit—the situated web of habits, affordances, and nerves that made past learning usable now. This is one of the core principles we are actively engineering at Robometrics® Machines in Robometrics® AGI.

For embodied AGI, the form factors are not aesthetics but parameters of cognition. A robot’s experiences are shaped by its actuators and reducers (stall torque, backlash, friction cones), its motors (torque-speed curves, thermal time constants), its limbs (link inertia, compliance, singularities), its skin (taxels, shear sensing, micro-vibration), its sensors (stereo baselines, f-θ optics, event cameras, inertial triads), and its control stack (whole-body QP, impedance, model-predictive torque limits). Learning is thus conditional on embodiment (E): the policy ( \pi(a,|,s,E) ) and the world model ( p(s_{t+1},|,s_t,a_t,E) ) must encode the body as a first-class variable. When we “swap bodies” without migration curricula, priors shatter: grasp reflexes tuned to viscoelastic fingertips fail on rigid pads; gait learned with 35 kg-cm hip modules stumbles with 28 kg-cm replacements. The fix is not a universal brain, but an embodiment-aware one—skills distilled into body-agnostic cores with thin adaptation layers that retune contact geometry, latency budgets, and energy envelopes in situ.

Training smarter models begins before the first reward. Pre-training should not be text-only; it must be sensorimotor. Build a multi-embodiment corpus where each sequence fuses vision, audio, touch, proprioception, and vestibular streams into synchronized tokens, with explicit interoceptive tags (motor current, temperature, battery internal resistance) so the network learns how doing feels. Pair generative world modeling (latent dynamics + decoder) with affordance prediction (contact, slip, yield) and homeostasis prediction (thermal headroom, fatigue). Replace stateless context windows with stateful memory: an episodic module that binds sensory slices to interoceptive summaries, a semantic store for skill schemas, and a consolidation process that replays “sleep”—off-policy rehearsal that privileges episodes where bodily state gated success. Retrieval, too, should be state-dependent: when the present feels like the past (similar heartbeat variability, joint stiffness, tactile spectrum), the system recalls the matching motor programs. Thus, pre-training does not just map the world; it maps how the world maps onto a particular body.

Post-training closes the loop. Use self-reinforcement learning in the wild with safety shields: curiosity drives propose tasks, model-predictive controllers keep the robot inside torque/temperature limits, and human interventions seed preference gradients only when necessary. Skills improve by active data selection—rehearsing under the bodily states in which they must later be recalled. Distill rolling checkpoints into a compact actor with an embodiment adapter (low-rank updates per limb, tactile adapters per skin) so knowledge survives hardware refreshes without losing its felt continuity. Memory stays stateful, not a stateless chat history: the robot tracks long-horizon physiological trends and uses them to schedule rest, recalibration, and consolidation, much as we do. This is how eternal life becomes artificial life: not an upload, but a continuity of sensation and skill across forms. “We can only see a short distance ahead, but we can see plenty there that needs to be done,” Turing warned. And as Asimov reminded us, “Science gathers knowledge faster than society gathers wisdom.” Smarter models will be the ones wise enough to remember with their bodies. At Robometrics® Machines, Robometrics® AGI implements this embodiment-aware, stateful stack—sensorimotor pre-training with interoceptive tags, state-dependent memory, safety-shielded self-reinforcement, and per-limb embodiment adapters—so skills carry across hardware without losing the feeling.