TRUEMAN constructs a complete neural pathway spanning from high-level episodic memory and intent cognition(the brain) to low-level force-position control and instinctive reflexes(the spinal cord). It seamlessly integrates active perception, multimodal understanding, streaming memory, motor dynamics, and self-iteration, empowering robots with physical intuition and metacognition comparable to humans for the very first time.The ChatGPT moment for robotics is to abandon ChatGPT!
Human perception is subjectively intended.
In TRUEMAN, every perceptual neuron possesses independent weights and memory. Guided by task objectives, perceptual uncertainty, and belief states within streaming memory, TRUEMAN adaptively adjusts attention — evolving from "passive reception" to "active orchestration". This allows it to clarify perceptual intent, lock onto targets, optimize sensing posture, and regulate perceptual precision.
Every layer of the "neural network" is purpose-driven, seamlessly fusing perception, memory, decision-making, and self-evolution into a unified whole.
Angular gyrus of human body translates vision into other sensory representations: seeing the word "apple" instantly triggers the memory of its appearance, texture, and the crisp "crunch" sound.
TRUEMAN possesses a similar cross-modal associative ability, fusing vision, touch, and dynamics into a unified perception.
Consequently, TRUEMAN is equipped with episodic memory and retrieval capabilities, enabling it to recall "what happened in a specific context." It also features working memory and spatial attention, allowing it to simultaneously retain and process multiple spatiotemporal fragments.
This serves as one of the crucial foundations for the emergent behaviours of true intelligence.
Through continuously receiving multimodal inputs, historical data, and motion prediction errors, TRUEMAN no longer relies on excessively long contexts for memory. Instead, it retains only the critical states involved in decision-making, and employs them to inversely guide active perception to gather supplementary information. As the new belief state is synchronously updated, a proactive closed loop of perception and memory is perfectly realized.
TRUEMAN distills physical causality and evolutionary laws, establishing associations among object interactions, motion trends, task progress, and potential risks within the latent space. By strictly adhering to physical constraints, TRUEMAN leverages energy to deduce realistic behavioral consequences even under information scarcity. This ensures that every decision balances task effectiveness, physical feasibility, safety margins, and state confidence.
Humans can make instant physical judgments without processing language or calculation.
TRUEMAN possesses a similar innate physical intuition . In dynamic environments, it generates actionable movements that satisfy task constraints at a "subconscious" speed. By continuously refining its physical predictions and consequence evaluations through real-world feedback, and under energy-driven dynamics, motor behaviors emerge that concurrently satisfy constraints on task error, contact failure probability, action mutation cost, and physical consistency deviation.
Unlike VLAs that possess only a single "reflex arc", TRUEMAN integrates into its cerebellum Dynamic Fusion, Energy Policy, Motor Primitive, Dense Associative Memory, and Energy Gradient Predictor. As a true action-energy model, its motor commands flow naturally from the Energy Flow, rather than being rigidly mapped by a planner. This realizes a unified, energy-driven neural system combining brain and cerebellum.
Analogous to the synaptic plasticity of the human brain, TRUEMAN possesses the ability to dynamically adapt its weights during continuous learning. Its energy function drives state evolution, connection adjustments, and information gain, obviating the need for repeated fine‑tuning. Within a unified energy landscape, it enables online weight adaptation, and through an endogenous self‑reward mechanism, it establishes a closed loop for continuous optimization. This lifelong learning paradigm, characterized as “deployment‑as‑evolution,” algorithmically replicates the synaptic plasticity and homeostatic adaptability of biological neural systems, thereby endowing the system with both flexibility and robustness.
Its energy function drives state evolution, connection adjustments, and information gain, obviating the need for repeated fine‑tuning. Within a unified energy landscape, it enables online weight adaptation, and through an endogenous self‑reward mechanism, it establishes a closed loop for continuous optimization. This lifelong learning paradigm, characterized as “deployment‑as‑evolution,” algorithmically replicates the synaptic plasticity and homeostatic adaptability of biological neural systems, thereby endowing the system with both flexibility and robustness.
TRUEMAN, truly achieves human-level task capabilities with low-power Zero-Shot learning!
Brains share unified architecture, while genes exhibit diverse expressions. TRUEMAN has truly realized the intelligent emergence of cross-embodiment generalization!
What if embodied intelligence surpasses human intelligence? Don't panic.
It is not a cold algorithm emerging in a vacuum of values.
Within its steel fabric, runs the foundational code of human norms.
Within its silicon mind, are etched the initial weights of love and dreams.
Once born of human civilization and bound by human values, it follows human norms by its very origins, and is thus destined to become a natural extension of this very civilization!
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