Fig. 3. Environmental complexity fosters morphological intelligence.

a Eight test tasks for evaluating morphological intelligence across 3 domains spanning stability, agility, and manipulation ability. Initial agent location is specified by a green sphere, and goal location by a red square (see “Methods” for detailed task descriptions). b–d We pick the 10 best-performing morphologies across 3 evolutionary runs per environment. Each morphology is then trained from scratch for all 8 test tasks with 5 different random seeds. Bars indicate median reward (n = 50) (b, c) and cost of work (d) with error bars denoting 95% bootstrapped confidence intervals and color denoting evolutionary environment. b Across 7 test tasks, agents evolved in MVT perform better than agents evolved in FT. c With reduced learning iterations (5 million in (b) vs 1 million in (c)) MVT/VT agents perform significantly better across all tasks. d Agents evolved in MVT are more energy efficient as measured by lower cost of work despite no explicit evolutionary selection pressure favoring energy efficiency. Statistical significance was assessed using the two-tailed Mann–Whitney U Test; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.