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A long-standing question in robot hand design is how accurate tactile sensing must be. This paper uses simulated tactile signals and the reinforcement learning (RL) framework to study the sensing needs in grasping systems. Our first experiment investigates the need for rich tactile sensing in the rewards of RL-based grasp refinement algorithms for multi-fingered robotic hands. We systematically integrate different levels of tactile data into the rewards using analytic grasp stability metrics. We find that combining information on contact positions, normals, and forces in the reward yields the highest average success rates of 95.4% for cuboids, 93.1% for cylinders, and 62.3% for spheres across wrist position errors between 0 and 7 centimeters and rotational errors between 0 and 14 degrees. This contact-based reward outperforms a non-tactile binary-reward baseline by 42.9%. Our follow-up experiment shows that when training with tactile-enabled rewards, the use of tactile information in the control policy’s state vector is drastically reducible at only a slight performance decrease of at most 6.6% for no tactile sensing in the state. Since policies do not require access to the reward signal at test time, our work implies that models trained on tactile-enabled hands are deployable to robotic hands with a smaller sensor suite, potentially reducing cost dramatically. 

Numerous studies have shown reduced performance in plants that are surrounded by neighbours of the same species^1,2, a phenomenon known as conspecific negative density dependence (CNDD)³. A long-held ecological hypothesis posits that CNDD is more pronounced in tropical than in temperate forests^4,5, which increases community stabilization, species coexistence and the diversity of local tree species^6,7. Previous analyses supporting such a latitudinal gradient in CNDD^8,9have suffered from methodological limitations related to the use of static data^10–12. Here we present a comprehensive assessment of latitudinal CNDD patterns using dynamic mortality data to estimate species-site-specific CNDD across 23 sites. Averaged across species, we found that stabilizing CNDD was present at all except one site, but that average stabilizing CNDD was not stronger toward the tropics. However, in tropical tree communities, rare and intermediate abundant species experienced stronger stabilizing CNDD than did common species. This pattern was absent in temperate forests, which suggests that CNDD influences species abundances more strongly in tropical forests than it does in temperate ones¹³. We also found that interspecific variation in CNDD, which might attenuate its stabilizing effect on species diversity^14,15, was high but not significantly different across latitudes. Although the consequences of these patterns for latitudinal diversity gradients are difficult to evaluate, we speculate that a more effective regulation of population abundances could translate into greater stabilization of tropical tree communities and thus contribute to the high local diversity of tropical forests.

 

One mechanism proposed to explain high species diversity in tropical systems is strong negative conspecific density dependence (CDD), which reduces recruitment of juveniles in proximity to conspecific adult plants. Although evidence shows that plant-specific soil pathogens can drive negative CDD, trees also form key mutualisms with mycorrhizal fungi, which may counteract these effects. Across 43 large-scale forest plots worldwide, we tested whether ectomycorrhizal tree species exhibit weaker negative CDD than arbuscular mycorrhizal tree species. We further tested for conmycorrhizal density dependence (CMDD) to test for benefit from shared mutualists. We found that the strength of CDD varies systematically with mycorrhizal type, with ectomycorrhizal tree species exhibiting higher sapling densities with increasing adult densities than arbuscular mycorrhizal tree species. Moreover, we found evidence of positive CMDD for tree species of both mycorrhizal types. Collectively, these findings indicate that mycorrhizal interactions likely play a foundational role in global forest diversity patterns and structure.

 

Jamming is a phenomenon in which a collectionof compliant elements is encased in an airtight envelope, anda vacuum-induced pressure enhances frictional and kinematiccoupling, resulting in dramatic changes in stiffness. This paperintroduces the jamming of square cross-sectioned fibers, whichallow for tunable and programmable anisotropic stiffness. Atheoretical model captures the effect of major geometric designparameters on the direction-variant bending stiffness of theselong and slender jamming elements. The model is experimen-tally validated, and a 13-fold stiffening in one direction anda 22-fold stiffening in the orthogonal direction is achievedwith a single jamming element. The performance of a square-fiber-jamming continuum robot structure is demonstrated in asteering task, with an average reduction of 74% in the measuredinsertion force when unjammed, and a direction-variant 53%or 100% increase in the measured tip stiffness when jammed. 

Flexures provide precise motion control without friction or wear. Variable impedance mechanisms enable adapt- able and robust interactions with the environment. This paper combines the advantages of both approaches through layer jamming. Thin sheets of complaint material are encased in an airtight envelope, and when connected to a vacuum, the bending stiffness and damping increase dramatically. Using layer jamming structures as flexure elements leads to mechan- ical systems that can actively vary stiffness and damping. This results in flexure mechanisms with the versatility to transition between degrees of freedom and degrees of constraint and to tune impact response. This approach is used to create a 2-DOF, jamming-based, tunable impedance robotic wrist that enables passive hybrid force/position control for contact tasks. 

Materials capable of dramatically changing their stiffness along specific directions in response to an external stimulus can enable the design of novel robots that can quickly switch between soft/highly–deformable and rigid/load–bearing states. While the jamming transition in discrete media has recently been demonstrated to be a powerful mechanism to achieve such variable stiffness, the lack of numerical tools capable of predicting the mechanical response of jammed media subjected to arbitrary loading conditions has limited the advancement of jamming-based robots. To overcome this limitation, we introduce a 3D finite–element-based numerical tool that predicts the mechanical response of pressurized, infinitely–extending discrete media subjected to arbitrary loading conditions. We demonstrate the capabilities of our numerical tool by investigating the response of periodic laminar and fibrous media subjected to various types of loadings. We expect this work to foster further numerical studies on jamming–based soft robots and structures by facilitating their design, as well as providing a foundation for combining various types of jamming media to create a new generation of tunable composites.