Baidu just announced its breakthrough ernie
AI now powers UBTECH's Walker S humanoids, unlocking seven separate game
changing abilities for AI robots as a result. But what can they do? Number one task breakdown and planning. One of Ernie's most impressive feats is its
ability to understand tasks linguistically, and then methodically break
them down into a series of actionable steps for Walker S to execute. This seamless integration of language
comprehension and task planning paves the way for robots to tackle increasingly intricate
challenges in real work environments. This paves the way for ability
number two soft object manipulation. Crucially, robots have always struggled with
manipulating soft, deformable objects like fabrics and
clothing. However, with Ernie's guidance, Walker S
demonstrated a remarkable level of dexterity by meticulously folding clothes
with its articulated hands. Importantly, this advancement opens
up a world of possibilities in both commercial and home
environments, but it's the next ability that sets it apart. Number three Intelligent Task Management
Baidu's Ernie doesn't just plan tasks, but instead it
coordinates and oversees the entire process, enabling Walker S to
autonomously manage and fulfill complex assignments. Even with nuances, this level of intelligent
task management is a game changer. By reducing the need for constant
human supervision and paving the way for truly autonomous robotics
applications that humans can set and forget. But this all hinges on ability. Number four semantic understanding and
interaction. Thanks to Ernie's natural language
processing capabilities, the robot can now comprehend the nuances of human speech
and respond accordingly. This ability to engage in thoughtful
interactions opens up exciting prospects for human robot collaboration and
automation across various domains. This leads to ability number five
multimodal environment understanding. Walker S boasts a wide range of sensors,
allowing the robot to perceive its surroundings in 3D and first person to
accurately identify nearby objects, enabling precise autonomous
operations like sorting, loading and more. The other half of its spatial awareness is
due to ability number six VLM based object pose recognition. Ernie also brings cutting edge computer
vision techniques to the table, assisting the Walker S humanoid in detecting
object poses with extreme accuracy to pave the way for even more
precise manipulation tasks, such as assembling intricate components in
manufacturing environments. Then there's number seven dynamic
interference recovery. Even in the most controlled environments,
unexpected interferences can occur. However, with Ernie's real time
coordination, Walker S can dynamically update its trajectories and
adapt to any obstacles or disturbances to ensure smooth and
uninterrupted operations. To note, Baidu is the equivalent of Google
in China, meaning the implications of this partnership with Ubtech are far
reaching. In the future, uptick aims to further
integrate various AI models and frameworks into its robots over the next 2
to 3 years, with plans to continue deploying Walker S humanoids on
production lines in Chinese factories this year. Additionally, uptick is set to launch its
first household companion robot by the end of 2024, bringing even more
advanced robotics right into our homes. Furthermore, through knowledge enhancement
techniques like knowledge internalization and external knowledge utilization, Baidu's
Ernie can also incorporate large knowledge bases and external data sources to
reason with real world knowledge. It also benefits from search enhancement by
tapping into Baidu's powerful semantic search capabilities to provide
timely, accurate reference information. Moreover, Ernie's dialogue
enhancement allows it to engage in more coherent, contextual
conversations by employing memory mechanisms and dialogue planning
abilities. Plus, there's Baidu's Paddlepaddle deep
learning platform, which enables efficient training of large language
models as well as optimized inference deployment. Importantly, Ernie's training data is mostly
focused on Chinese practical applications and broad
knowledge domains, with English likely coming in the future, but no
word yet as to when. As this powerful language model continues to
evolve through user feedback and integration with Baidu's technology
stack, it holds immense potential to drive AI powered innovation
across numerous industries. Beyond robotics. Meanwhile, in another leap forward for
robotics, Google DeepMind is demonstrating a pioneering new approach
called Language Model Predictive Control, or Lmpc, which has groundbreaking
implications. For too long, the promise of natural
language interfaces for intuitive robot control has been hindered by the
inability of large language. Which models to retain contextual
information over extended multi-turn interactions, leading to a frustrating
inability to remember previous instructions. But Lmpc aims to finally break
this barrier by enhancing the very teachability of LMS for
robotic tasks. By enabling continuous context retention
from prior interactions, it promises to drastically reduce the average
number of language inputs required for a robot to grasp and execute complex,
multi-step commands conveyed through natural conversation alone. But the key innovation here is in treating
human robot language exchanges as a partially observable Markov decision
process. This novel framing allows the LM to
proactively predict the trajectory of future interactions, integrating this
predictive prowess with classical robotics techniques like model predictive
control. The result is a framework that empowers
robots to anticipate forthcoming instructions and plan optimal real time
actions accordingly. Yet Lpc's true strength lies in its dual
pronged learning strategy while leveraging In-context
adaptation for rapid responsiveness during live exchanges. It concurrently engages in continual model
fine tuning to bolster long term generalization capabilities. This powerful synergy transcends the
limitations of conventional approaches tethered to specific training scenarios,
paving the way for robust performance across diverse robotic embodiments and
tasks, even ones never encountered during training. After extensive evaluations and blind
testing, the researchers validated Lpc's unparalleled ability to
enhance teachability compared to existing baselines. But its usefulness extends even further,
demonstrating remarkable generalization to previously unseen tasks
and robotic APIs to. Moreover, a top user conditioned variant
amplifies performance universally by intelligently
prioritizing input from expert human instructors. Propagate their proficiency throughout the
system. While the outcomes are undeniably promising,
the researchers acknowledge inherent limitations that spark
opportunities for further exploration, detailed in forthcoming materials, and to
accelerate progress within this blossoming field of natural language
human robot interaction. They've released a comprehensive trove of
code, datasets and video demonstrations. One result will be to
democratize robot programming by making natural language the
ultimate interface, granting seamless control to non-experts across
manufacturing, healthcare, exploration and other sectors. But perhaps its greatest impact will be
catalyzing the elusive dream of fluent human robot symbiosis by conquering
the contextual amnesia that has long plagued language based
robot instruction. As this catalyst research moves forward, we
are likely about to witness the dawn of an era where robots become tireless
students of human language and behavior.
