In this post I will describe several open fundamental issues and some of the critical software challenges which were highlighted and remain unresolved:
𝟭. 𝗣𝗼𝗹𝗶𝗰𝘆 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗦𝗮𝗳𝗲𝘁𝘆
Robotics still lacks robust mechanisms for real-time failure detection, safety guarantees under learned policies, and predictable behavior in out-of-distribution conditions
𝟮. 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 𝗥𝘂𝗻𝘁𝗶𝗺𝗲 𝗕𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀
Model inference is improving rapidly; simulation performance is not. Python-based environments remain a major constraint, and high-speed C++/GPU simulators are still nascent.
𝟯. 𝗧𝗵𝗲 𝗛𝘂𝗺𝗮𝗻–𝗥𝗼𝗯𝗼𝘁 𝗘𝗺𝗯𝗼𝗱𝗶𝗺𝗲𝗻𝘁 𝗚𝗮𝗽
Significant progress has been made, but mapping human intent onto robot morphology continues to be a major open challenge—especially for contact-rich or bimanual manipulation.
𝟰. 𝗟𝗼𝗻𝗴-𝗛𝗼𝗿𝗶𝘇𝗼𝗻, 𝗠𝘂𝗹𝘁𝗶-𝗦𝘁𝗲𝗽 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻
Memory remains a limiting factor. Retrieval-based methods represent progress, but long-sequence stability is unresolved for most architectures.
𝟱. 𝗨𝘀𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝗳 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲
The ecosystem remains fragmented. Despite numerous advancements, there is no unified, developer-friendly stack equivalent to “the PyTorch of robotics.”
𝘛𝘩𝘦 𝘣𝘰𝘵𝘵𝘰𝘮 𝘭𝘪𝘯𝘦
Robotics is entering a period of accelerated capability—but progress is constrained less by hardware and more by software infrastructure, data engineering, and simulation bottlenecks.
👉 The largest opportunity now is to build the scalable, reliable software layer that bridges today’s innovations with real-world deployment at scale.
This post was first published on LinkedIn in November 2025.
