Sensorimotor Skills Precede Language: True, robust AI must first master physical interaction, sensory perception, and motor control before developing advanced language, mirroring the developmental path of humans.
Quadrupedal Locomotion is Nearly Solved: Through techniques like Rapid Motor Adaptation (RMA), the challenge of enabling four-legged robots to walk and adapt to varied, uneven terrain is largely resolved.
Bipedal Locomotion Remains a Grand Challenge: Unlike its four-legged counterpart, two-legged locomotion is significantly harder due to the complexities of balance and is still an unsolved problem in robotics.
LLMs are Powerful but Ungrounded: While models like GPT-4 demonstrate impressive text-processing capabilities, they lack the grounding in physical, sensory-motor experience required for true, non-brittle intelligence.
AI Research is Bifurcating: The field is splitting into corporate-led 'big science' for training massive models and university-led 'small science' for conceptual exploration, making it impossible for academia to compete on model scale.
Foundational Belief
Malik grounds his research philosophy in evolutionary biology, citing fossil data showing that the human hand's evolution preceded the brain's, forming the basis for his 'embodiment-first' approach to AI.
Core Research Strategy
He bets his scientific career on the approach of developing physical, sensorimotor intelligence in AI before focusing on language, believing this is the path to fully realized AI.
Key Technical Development
Malik and his collaborators develop Rapid Motor Adaptation (RMA), a technique for robot locomotion that uses the gap between commanded and sensed motion to adapt to new terrain in less than a second.
Empirical Validation
The RMA policy is proven effective, allowing a robot dog to traverse diverse terrains. During this work, his group discovers that vision is a necessary component for more complex tasks like climbing stairs, which the blind version could not handle.
State of the Field Assessment
He assesses the current state of robotics and computer vision, declaring quadrupedal locomotion nearly solved but identifying bipedal locomotion and single-image 3D reconstruction as major remaining challenges.
Broader AI Commentary
Malik comments on the broader AI landscape, acknowledging the power of LLMs like GPT-4 while critiquing their lack of grounded experience and noting the structural split in AI research between corporate 'big science' and academic 'small science'.
▶Embodiment First: A Developmental Approach to AIMay 2026
Malik's core thesis is that AI development should mirror human evolution, where physical intelligence (e.g., dexterity from an opposable thumb) preceded complex language. He bets his career on the idea that grounding AI in sensory-motor competencies like seeing, moving, and manipulating objects is the necessary foundation for creating robust, less brittle intelligence.
This 'embodiment-first' strategy represents a contrarian bet against the dominant language-first paradigm, suggesting that long-term AGI investors should consider robotics and sensorimotor research as critical, potentially undervalued, enabling technologies.
▶Rapid Motor Adaptation (RMA) as a Robotics BreakthroughMay 2026
Malik details the development of RMA, a technique enabling a robot to adapt its movements to changing terrain in under a second. The system's core principle is to use the discrepancy between a commanded action and the actual sensed motion to infer terrain properties and adjust its policy, allowing a single neural network to navigate everything from rocky riverbeds to muddy piles.
RMA's success in creating a single, adaptable policy for diverse terrains is a significant step toward deploying autonomous robots in unpredictable, real-world environments, moving beyond the structured confines of factories.
▶Mapping the Frontier: Solved vs. Unsolved Problems in AIMay 2026
Malik provides a clear assessment of the current state of AI capabilities, declaring quadrupedal locomotion 'pretty close to solved.' Conversely, he identifies bipedal locomotion and recovering a human-level 3D model from a single image as major unsolved challenges, illustrating the uneven progress across different domains of AI research.
Malik's delineation of solved and unsolved problems offers a valuable roadmap for R&D, signaling to analysts that bipedalism and advanced single-image 3D vision are high-impact areas ripe for future breakthroughs and investment.
▶The Bifurcation of AI Research: 'Big Science' vs. 'Small Science'May 2026
Malik characterizes the AI research landscape as splitting into two camps: the 'small science' of individual-led exploration common in universities, and the 'big science' of massive, resource-heavy projects like training GPT-4 or Meta's Segment Anything, which are now the exclusive domain of large corporations. He explicitly states that training a GPT-scale model is no longer feasible in an academic lab.
This trend suggests a consolidation of power in foundational model development within a few corporate entities, potentially creating a bottleneck for academic innovation and shifting the role of universities toward theoretical work and niche applications.