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		<title>Robots Read Pushes and Obstacles in the Shape of Their Own Prediction Errors</title>
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		<pubDate>Thu, 15 Oct 2026 23:08:15 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[Technology]]></category>
		<category><![CDATA[active inference]]></category>
		<category><![CDATA[cognitive robotics]]></category>
		<category><![CDATA[embodied robotics]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[motor babbling]]></category>
		<category><![CDATA[neurorobotics]]></category>
		<category><![CDATA[perturbation classification]]></category>
		<category><![CDATA[prediction error]]></category>
		<category><![CDATA[predictive processing]]></category>
		<category><![CDATA[proprioception]]></category>
		<category><![CDATA[sensory attenuation]]></category>
		<category><![CDATA[servo current]]></category>
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					<description><![CDATA[Researchers at Lund University show that a robot can identify pushes, sustained forces, and obstacle collisions with up to 98.5 percent accuracy by analyzing the temporal shape of its own servo-current prediction errors, without cameras, touch sensors, or force gauges.]]></description>
		
		
		
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