5 Key Challenges Limiting Robotic Dexterity

Full-hand tactile feedback, which is essential for robust grasping and slip detection, generates large volumes of data that need rapid processing for in-hand object tracking, force estimation, and collision avoidance. In addition to measuring force magnitude, sensors also need to identify surface textures and slippage, tasks that the human nervous system can readily perform through complex and rich feedback loops.¹

The transition from simulation-based manipulation strategies to real-world applications involves additional challenges. Current tactile sensor arrays often struggle with durability and sensitivity in unpredictable environments, such as varying object sharpness or the presence of contaminants. Real-world experiments reveal significant discrepancies between idealized, simulation-based results and practical performance, particularly when robots attempt to generalize across object shapes or grasp configurations.¹

The complexity is further compounded by the integration challenges of fusing tactile data with vision and proprioception, all while maintaining real-time response for dynamic, noise-rich environments. These constraints mean that even advanced systems continue to experience high rates of grasp failure and suboptimal adaptability. This shows that tactile perception is still one of the biggest hurdles for improving robotic dexterity today.¹

2. Adaptive Motor Control

Human hands easily adjust their grip strength and finger positioning based on the size and texture of various objects. To replicate this adaptability in robots, advanced control systems are needed to respond to both expected and unexpected changes in tasks or environments. For highly articulated robotic hands, managing the degrees of freedom and coordinating movements can be quite challenging.

Traditional control strategies, which rely on analytical models to issue motor commands, often break down as system complexity grows. This is especially true for highly articulated hands, where friction, backlash, mechanical compliance, and environmental variability introduce nonlinearities that challenge classical methods. Sensor noise, actuator delays, and uncertainty in system models further complicate real-time responsiveness.^1,2

In practice, real-world tasks - such as manipulating multiple objects simultaneously or interacting with complex environments - introduce forces and deformations that can easily disrupt pre-planned motions. These scenarios demand rapid online adaptation, which depends on sensorimotor architectures built for continuous feedback, self-calibration, and fault tolerance.

While there has been some progress in integrating adaptive controllers, a major performance gap still exists between robots and the intuitive motor skills of humans. This gap is most evident in tasks demanding both force control and high degrees of finger independence, like threading a needle or manipulating soft, irregular shapes.?^1,2

3. High-Dimensional Planning

Dexterous hands, whether human or robotic, are made up of a number of different joints and actuators. With each added degree of freedom, the complexity of planning precise and feasible movements grows exponentially. This problem becomes more pronounced when interacting with environments that are unmodeled or only partially known. Compared to simpler grippers, generating smooth, efficient, and collision-free trajectories for multi-jointed hands demands far greater computational resources. 

In these high-dimensional spaces, planners often face the “curse of dimensionality,” where the search for optimal, or even viable, solutions becomes computationally intensive.^2,3

To overcome these limitations, roboticists have been increasingly investigating learning-based alternatives to traditional, equation-driven trajectory planning. Methods like deep reinforcement learning and imitation learning have shown promise, offering more flexibility in uncertain and dynamic environments. However, these models typically depend on large volumes of high-quality demonstration data, which are difficult to collect at scale. Existing datasets often stem from simplified tasks or limited teleoperation, making them poorly suited for training policies that can handle real-world complexity. ³

The high dimensionality of dexterous hands also poses major challenges for generalizing and transferring learned skills. Without access to diverse, realistic training examples, models tend to overfit to narrow scenarios, limiting their usefulness in practical applications. As a result, planning remains one of the most daunting barriers to true robotic dexterity, with a persistent performance gap between simulations and real-world deployment.³

4. Robust Learning Frameworks

Current advances in dexterous robotic manipulation have been heavily reliant on machine learning methods that ultimately have fundamental limitations. Most manipulation skills are developed through either reinforcement learning, which refines behavior via reward-based exploration, or imitation learning, which replicates human demonstrations.⁴

Each method, however, comes with challenges.

Reinforcement learning tends to overfit to the highly controlled conditions of simulations and often fails to generalize to the unpredictability of the real world. Imitation learning benefits from human expertise but is constrained by the mismatch between human and robotic kinematics - a problem commonly referred to as the “human-to-robot gap.” In addition, these frameworks typically encode manipulation strategies in a static way, lacking mechanisms for real-time adaptation or seamless task-switching.⁴

Another key limitation is the weak integration of human feedback and intent recognition. While human-in-the-loop systems exist, they’re often not designed for efficient, real-time input. As a result, robots rarely receive meaningful corrections or intent cues during learning, which limits their ability to refine behaviors interactively.

Both reinforcement and imitation learning pipelines also depend on access to large, diverse datasets that reflect the wide range of manipulations encountered in practice. Generating such datasets is costly and time-intensive, particularly when realistic, annotated examples are needed. When tasks or environments deviate from those in the training set, performance tends to degrade sharply.

Addressing these gaps calls for new strategies that can accommodate physiological feedback and enable robots to align their behavior intuitively with human intent, while also strengthening learning frameworks to resist distributional shifts.^3-5

5. Actuator Complexity and Mechanical Limitations

Dexterous manipulation relies on the ability to precisely actuate multiple joints, each with fine force control and rapid feedback, in a compact form factor. Modern actuators, whether electric, pneumatic, hydraulic, or made from exotic materials, must balance various competing demands. These include high speed, precision, strength, lightweight construction, low energy consumption, and durability under stress. As robotic hands add more degrees of freedom, they face trade-offs between size, rigidity, weight, and overall performance. Simplifying the mechanical design often limits the range of motion or reduces force output, while more complex systems introduce new challenges in control and reliability.^6,7

Actuation systems also face degradation over time. Factors like mechanical wear, limited backdrivability, inertial loading, and inherent nonlinearities reduce consistency and accuracy, especially during long-term deployments. These problems become even more pronounced in real-world conditions, where robots are exposed to dust, fluids, temperature shifts, and other environmental stressors. 

Emerging advances in soft robotics and bioinspired actuation have pushed the field forward. Tendon-driven mechanisms, compliant structures, and variable-stiffness actuators aim to replicate the adaptability of biological muscles and joints. Yet, as actuation complexity grows, so do the challenges related to control and integration, hindering progress toward fully biomimetic hands.

Effective real-time coordination between multiple actuators, especially under varying loads or partial system failure, remains a prominent barrier. This drives the search for materials, structures, and algorithms that can provide both versatility and robustness.^6,7

Conclusion

Solving the barriers to robotic dexterity will require breakthroughs across perception, control, learning, planning, and actuation. An ideal dexterous robotic hand should integrate dense multimodal sensing to mimic human tactile sensitivity. It must also enable continuous adaptation of sensorimotor skills in complex and uncertain environments. Furthermore, it should be capable of robust planning in high-dimensional spaces while achieving minimal failure rates.

Each of these challenges, while distinct, is interconnected.

Progress in one area can enhance advancements in others, but this synergy requires collaboration across various disciplines such as mechanical engineering, computer science, neuroscience, and human-robot interaction.

As the field progresses, solutions must remain grounded in empirical validation using real-world benchmarks, with open sharing of datasets and experimental results to allow comparative analysis. Progress in these domains is critical for robotics to reach new heights of dexterity, enabling applications that range from complex surgeries to household assistance and challenging extraterrestrial exploration.

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References and Further Reading

1. Zhao, Z. et al. (2025). Embedding high-resolution touch across robotic hands enables adaptive human-like grasping. Nature Machine Intelligence, 7(6), 889-900. DOI:10.1038/s42256-025-01053-3. https://www.nature.com/articles/s42256-025-01053-3
2. Wang, G. et al. (2025). Smooth, rigid, and dexterous robotic machining path planning based on on-site scanned point clouds. Robotics and Computer-Integrated Manufacturing, 98, 103114. DOI:10.1016/j.rcim.2025.103114. https://www.sciencedirect.com/science/article/abs/pii/S0736584525001681
3. Li, G. et al. (2025). The Developments and Challenges towards Dexterous and Embodied Robotic Manipulation: A Survey. ArXiv. DOI:10.48550/arXiv.2507.11840. https://arxiv.org/abs/2507.11840
4. Nahavandi, S. et al.(2024). Machine learning meets advanced robotic manipulation. Information Fusion, 105, 102221. DOI:10.1016/j.inffus.2023.102221. https://www.sciencedirect.com/science/article/pii/S1566253523005377
5. Gao, B., Fan, J., & Zheng, P. (2025). Empower dexterous robotic hand for human-centric smart manufacturing: A perception and skill learning perspective. Robotics and Computer-Integrated Manufacturing, 93, 102909. DOI:10.1016/j.rcim.2024.102909. https://www.sciencedirect.com/science/article/abs/pii/S0736584524001960
6. Broti, N. M. et al. (2025). Design and development of a dexterous soft-robotics based assistive exoglove with kinematic modeling. Intelligent Systems With Applications, 27, 200550. DOI:10.1016/j.iswa.2025.200550. https://www.sciencedirect.com/science/article/pii/S2667305325000766
7. Zhang, N. et al. (2025). Soft robotic hand with tactile palm-finger coordination. Nature Communications, 16(1), 1-14. DOI:10.1038/s41467-025-57741-6. https://www.nature.com/articles/s41467-025-57741-6

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